{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capital Bikeshare自行车数据探索项目报告\n",
    "\n",
    "该数据集来自UCI机器学习知识库。Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析，训练数据为2011年的数据，要求预测2012年每天的单车共享数量。\n",
    "本项目将原始数据集存为csv格式，方便调用pandas做数据分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# path to where the data lies\n",
    "#dpath = './data/'\n",
    "data = pd.read_csv(\"day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除特征\n",
    "特征casual,registered与cnt都是要预测的y，但因题目要求，所以去掉casual,registered两个特征。\n",
    "特征中，instant为序号，dteday为日期，无法对cnt造成影响，所以也删掉这两个特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  yr  mnth  holiday  weekday  workingday  weathersit      temp  \\\n",
       "0       1   0     1        0        6           0           2  0.344167   \n",
       "1       1   0     1        0        0           0           2  0.363478   \n",
       "2       1   0     1        0        1           1           1  0.196364   \n",
       "3       1   0     1        0        2           1           1  0.200000   \n",
       "4       1   0     1        0        3           1           1  0.226957   \n",
       "\n",
       "      atemp       hum  windspeed   cnt  \n",
       "0  0.363625  0.805833   0.160446   985  \n",
       "1  0.353739  0.696087   0.248539   801  \n",
       "2  0.189405  0.437273   0.248309  1349  \n",
       "3  0.212122  0.590435   0.160296  1562  \n",
       "4  0.229270  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['instant','dteday','casual','registered'],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分割\n",
    "将2011年的数据作为训练数据，将2012年的数据作为测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data[data.yr==0].drop('yr',axis=1)\n",
    "test = data[data.yr==1].drop('yr',axis=1)\n",
    "x_train = train.drop('cnt',axis=1)\n",
    "x_test = test.drop('cnt',axis=1)\n",
    "y_train = train['cnt']\n",
    "y_test = test['cnt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 12 columns):\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 68.6 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season        0\n",
       "yr            0\n",
       "mnth          0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "cnt           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### 查看是否有空值\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看数据各特征的分布，以及特征之间是否存在相关关系等冗余。\n",
    "\n",
    "我们可以借用可视化工具来直观感觉数据的分布。\n",
    "\n",
    "在Python中，有很多数据可视化途径。\n",
    "Matplotlib非常强大，也很复杂，不易于学习。 \n",
    "Seaborn是在matplotlib的基础上进行了更高级的API封装，从而使得作图更加容易，在大多数情况下使用seaborn就能做出很具有吸引力的图，而使用matplotlib就能制作具有更多特色的图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           season          yr        mnth     holiday     weekday  workingday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     2.496580    0.500684    6.519836    0.028728    2.997264    0.683995   \n",
       "std      1.110807    0.500342    3.451913    0.167155    2.004787    0.465233   \n",
       "min      1.000000    0.000000    1.000000    0.000000    0.000000    0.000000   \n",
       "25%      2.000000    0.000000    4.000000    0.000000    1.000000    0.000000   \n",
       "50%      3.000000    1.000000    7.000000    0.000000    3.000000    1.000000   \n",
       "75%      3.000000    1.000000   10.000000    0.000000    5.000000    1.000000   \n",
       "max      4.000000    1.000000   12.000000    1.000000    6.000000    1.000000   \n",
       "\n",
       "       weathersit        temp       atemp         hum   windspeed          cnt  \n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000   731.000000  \n",
       "mean     1.395349    0.495385    0.474354    0.627894    0.190486  4504.348837  \n",
       "std      0.544894    0.183051    0.162961    0.142429    0.077498  1937.211452  \n",
       "min      1.000000    0.059130    0.079070    0.000000    0.022392    22.000000  \n",
       "25%      1.000000    0.337083    0.337842    0.520000    0.134950  3152.000000  \n",
       "50%      1.000000    0.498333    0.486733    0.626667    0.180975  4548.000000  \n",
       "75%      2.000000    0.655417    0.608602    0.730209    0.233214  5956.000000  \n",
       "max      3.000000    0.861667    0.840896    0.972500    0.507463  8714.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 各属性的统计特性\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此处得到各属性的样本数目、均值、标准差、最小值、1/4分位数（25%）、中位数（50%）、3/4分位数（75%）、最大值\n",
    "可初步了解各特征的分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 单变量分布分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xb57e710>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb404a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y（房屋价格）的直方图／分布\n",
    "plt.scatter(range(data.shape[0]),data.cnt.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这是目标cnt的散点图分布，明显可以看到在[4000,5000]和[6000,8000]的数据很多，说实话，不是特别符合高斯分布的规律，原因暂时存疑。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb54c3c8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb5a3ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "sns.distplot(data.cnt,bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大体形状还是符合高斯分布的，但在2000和8000左右都各有一个突增点。结合散点图，可以看到0附近和8200的地方有一些离群点，数据处理时可以考虑去掉。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来观察数值型特征的分布。\n",
    "\n",
    "常识判断，温度temp和体感温度atemp的关联性应该是比较强的，因此将两个特征的散点图一起展示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xb6f4fd0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb637e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data.temp.values)\n",
    "plt.scatter(range(data.shape[0]),data.atemp.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "果然，两个特征的分布形状大致相同。其实在计算相关性之前，就可以判定两个特征是冗余的了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xb74ef98>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb6d8518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]),data.hum.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hum的分布是相对集中且均匀的，有一些离群点需要处理，但在散点图上看不出明显的分布，所以看一看它的直方图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb703e48>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb69c198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.hum,bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "形状有点像高斯分布，但高峰值拉的比较长，和cnt的分布不是特别像"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb78a668>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CEs40d3PgbLvXUUQmVERj5GbmN7OdQD3wjHNuc3RjCcD2k8309Ie4Zlah11Hiwo3zSzCDZ3RykCSZiIrcOTfonFsGTAVWm9miCx9jZveY2VYz29rQoPm84+Glw40EfMZaHeiMSFF2KsvK81TkknRGNWvFOdcCvAC8+yJfW+ecq3LOVRUVaShgPLxypIEV0/LJSg14HSVu3LSghD3VrdS2dnsdRWTCRDJrpcjM8sK304F3AgeiHSzZNXX2sbemjWtma1hlNG5eUAKgtVckqUSyR14KPG9mu4HXGBojfzy6seTVI404h4p8lGYWZTGjMJNn9td7HUVkwoz4f3bn3G5g+QRkkWFeOdxITlqAJWW5XkeJK2bGTQtK+NGrx2nv6Sc7Leh1JJGo05mdMcg5x8uHG7h6ZiEBv96i0bppQQn9g44XD+mguyQHtUQMOlTXQU1rD++Yq4PGY7FiWj6TMlM0e0WShoo8Bj13YGh89x1ziz1OEp+GFtEq5nktoiVJQkUeg54/WM+C0hydln8Fzi+itUWLaEkSUJHHmNbufradbOb6eRpWuRLXzg4voqXhFUkCOtPEY+s3n3rT53uqWxkMOa6/YFjlwsfJm13s7+fa2UOLaP39+xZgZh6kEpkY2iOPMQfPtpObHmRZeZ7XUeLezQsmU93SzZ7qVq+jiESVijyGhJzjUF07180p0rTDcfCuhZNJ8fv41fZqr6OIRJXaIobUtHTT0TvA9Zp2OC5yM4LcOL+Yx3bVaPaKJDQVeQzZX9uGoWmH4+kDy8s419nHy4d1cpAkLhV5DNlf2870gkwmZaZ4HSVhvGNuMfkZQQ2vSEJTkceIps4+zrb1sGBKjtdREkpKwMd7l0zhmX11tPX0ex1HJCpU5DFif20bAAtKVeTj7YMryugdCPGbPbVeRxGJChV5jNhX20ZJTqqGVaJgWXkes4qzWL/ltNdRRKJCRR4DunoHONHYqb3xKDEzPrFmGrtOt7D7TIvXcUTGnc7sjAEH6tpxwPxwkesszvH3wZVT+eenDvKzjSf5l4/oZCtJLNojjwH7atrISQtQlpfudZSElZMW5P3Ly9iwq4bmzj6v44iMKxW5x3r7BzlU187CslytBxJlf3TVdHoHQjy0TWPlklhU5B47UNfOQMixaIou6RZt8ybnsLpiEj/bdJIBnekpCURF7rHXq1vJTgswvSDD6yhJ4VPXzuB0UzeP79ZUREkcKnIPdfUNDA2rTMnBp2GVCXHT/BLmlmTzb88fIRRyXscRGRcqcg89f6CB/kENq0wkn8/4/A2zOFLfwW/3nvU6jsi4UJF76Mk9tWSmBqgozPQ6SlK5ZXEplYWZfPe5IzinvXKJfypyj3T1DfDcgXoNq3jA7zP+4vpZ7K9t46m9uhScxL8RTwgys3Lgp8BkIASsc859O9rBEt2z++vp7h9kSZmGVSbS+ZOtBkOOouxU/vbXe7hhXjEpAe/3aS52ItjH1kzzIInEm0j+9Q4Af+2cmw+sBT5nZguiGyvxPbqjmsk5aRpW8YjfZ9yyqJRznX38dOMJr+OIXJERi9w5V+uc2x6+3Q7sB8qiHSyRNXf28eKhBm5bNkXDKh6aOzmb2cVZfOd3h3W2p8S1Uf1/0swqgOXA5miESRZPvl7LQMhx29IpXkdJeu9ZXEpH7wDfevaQ11FExiziIjezLOAR4C+dc20X+fo9ZrbVzLY2NOiyWpfz6M4aZhVnsVAXkfDc5Jw0Prl2Oj/bdJIdp5q9jiMyJhGtfmhmQYZK/OfOuV9d7DHOuXXAOoCqqirN6bqEmpZuthxv4q9vmqO1VWLEl981l6f31fGVR/bw2BeuISXg04FHiSsj7pHbUNv8J7DfOffN6EdKbBt21QBw2zINq8SK7LQg//j+RRysa+cHLx71Oo7IqEUytPI24JPADWa2M/xxS5RzJaxHd9awrDyP6QWarRJLbpxfwvuWTuHfnjvyxmX3ROJFJLNWXnHOmXNuiXNuWfjjyYkIl2gO1bWzv7aN92tvPCb9r/ctIDcjyBcf2EG/VkeUOOL9WRBJZMPOGnwGty5RkceigqxUvvGRpRyu7+BJXahZ4ogu9TZBnHM8uquat80qpCg71es4SWEsl8y7bk4Rn75mBve9cpw5JdlvXH5PJJZpj3yCbD/Vwummbt6/TOdSxbr//u65lOam8cj2M7T19HsdR2REKvIJsmFnNakBHzcvLPE6iowgNeDno1Xl9A+GeHjbGUJaIVFinIp8AvQNhHhsdy3vnF9CdlrQ6zgSgeKcNG5ZXMqR+g5ePdLodRyRy9IYeZQMH5/dV9NGU2cfH1qpYZVYdKmx9NUVkzhc18HTe+uo0HRRiWHaI58A2081k5Ua4LrZRV5HkVEwMz64oozs9AAPvHaKli4trCWxSUUeZZ29Axw8286y8jwCfv11x5uMlAB3rZpGe/cAX35ot64oJDFJzRJlu860MOgcy6fleR1Fxqh8UgbvXjSZZ/fXcd/Lx72OI/IWGiOPsh2nWpiSm0ZpbrrXUeQKXD2zgIFQiH/67QFWTM9n5fR8ryOJvEFFHkVn23qobunm1sWlwNhOUJHYYGasrihgy/Em/vTHr/GF62fx6esqvY4lAmhoJap2nGzGZ7C0XMMqiSA9xc9dq6fR0TvAQ9vOEAppvFxig4o8SgZDjp2nW5g7OYesVP3HJ1FMzc/glkWTOVjXzr0vHfM6jgigIo+aI/XttPcOsFIHORPO2soCFpXl8v89fZDXTjR5HUdERR4t20+1kJHiZ87kbK+jyDgzMz64vIyp+el8fv126tt7vI4kSU5FHgWtXf3sr21j6dQ8Aj79FSeitKCf9y2ZQlNnHx/+/kZ++vsTOpgtnlHLRMFju2sYCDlWaIpaQpuSl86HVkzlVFMXG3bV6GQh8YyKPAoe2nqakpxUpuSmeR1FomzJ1DzeMbeIrSebefXoOa/jSJJSkY+zvTWt7DrTyqqKSQxdt1oS3Tvnl7BwSg5P7qnl0Z3VXseRJKQiH2cPbjlNSsDHMs0dTxo+M+6oKmdGYSZffmgXLx1q8DqSJBkV+Tjq7hvkv3ZWc+viUjJSNHc8mQT9Pj65djqzirO552dbef5AvdeRJImoyMfRE3tqae8Z4M5V5V5HEQ+kBf3c/6nVzCrO4jM/3cqGXTVeR5IkoSIfRw9sOUVlUSarZ0zyOop4pCArlfWfWcuKafl86cEdfOd3hxnUqfwSZSrycXKorp1tJ5u5a9U0HeRMcjlpQX7yp6u5fekUvvnMIe7+0RYa2nu9jiUJTEU+Th7ccpqgf+iKMiLpKX6+9dFlfP2Di9lyvIkbv/ECP/n9CQYGQ15HkwQ0YpGb2Q/NrN7MXp+IQPGop3+QX+04w80LJ1OQlep1HIkRZsadq6fxxBevYfHUXP5+w15u/c4rbNhVo0KXcRXJHvmPgXdHOUdce2rvWVq6+rlr1TSvo0gMmlWczf2fWsP3P76C/lCILz6wg+u/8QI/23iCnv5Br+NJAhixyJ1zLwFa4u0yHthyimmTMrh6ZoHXUSRGmRnvWVzKs//t7dz7yZUUZqXyPx/dy9u+/hzf+d1hXdhZrsi4TXY2s3uAewCmTUuePdNjDR1sOtbEf3/XXHw+HeSUy/P5jHctnMzNC0rYcryJH7x4lG8+c4gfvHiU5eV5vH1usdavl1Ebt38xzrl1wDqAqqqqpJlvtX7zKXwGfp9p9TuJmJmxprKANZUFHDjbxr0vHuPRndVsO9XMjfNKWFtZgF87BhIh/eq/Ap29A/xi62kWleWSkxb0Oo7EqXmTc/jWR5cxfVIGT+yp5Yk9tWw/1cwdVTqxTCKj6YdX4Fc7qmnvGeDqSo2Ny5Urzknj7qsr+MSaabR19/O954/ws40ntDyujCiS6YcPABuBuWZ2xsw+Ff1Ysc85x49fPc7islzKJ2V4HUcShJmxYEouX7xxNpVFmfzPR/fylUf20Deg6YpyaSMOrTjn7pqIIPHmlSONHG3o5BsfWUqvfshknGWnBfmjqyqoa+vhu88d4WRTJz/4xEryMlK8jiYxSGPko3T+gOZPfn+CzNQAnb0DBPwaoZJLu9hB8I+tGXlml8+Mv755LpVFmfzNw3u4c90mfv7pNTrpTN5CDTQGdW09HKxrZ82MSSpxiboPLJ/KfX9cxYlzndy5bpMu9ixvoRYag5cONRD0G1fpIKdMkOvmFPGju1dT3dLNnfduora12+tIEkNU5KPU3NXHrjMtrKqYRKZO3JAJdNXMAn76p6upb+/ljns3crqpy+tIEiPURKP0ypFGAK6ZVehxEolFkZ4UNtaTx6oqJnH/p9fwR/+5+Y0x84rCzDE9lyQO7ZGPQlNnH1tPNLGsPE+zB8Qzy8rzWP+ZtXT1DXDHvRs5XNfudSTxmIp8FL7/whEGBh3XzS7yOookuUVlufziz67CAR9dt4m9Na1eRxIPqcgjVNvazU82nmT5tDyKc9K8jiPCnJJsfvlnV5EW8HHXuk3sONXsdSTxiIo8Qt/53RGcc9w4r8TrKCJvmFGYyS8/exX5mSl84r7NbD52zutI4gEd7IzA8cZOfrn1NJ9YM438TI2Ny8SJ9KDoXaum8Z+vHufj923mQyun8k8fWhLlZBJLtEcegX/+7QGCfuNzN8zyOorIReWkB/nMtZWU5afzi9dO842nDxIKabGtZKEiH8ELB+v5zetn+fz1syjO1ti4xK6s1ACfumYGVdPz+e5zR/jjH22hrk1ngSYDFfll9PQP8vcb9lJZmMlnrqv0Oo7IiAI+Hx9YXsb//cBiXjvRxLv+9SUe312jpXATnIr8Mn7w4lFOnuvia7cvJDXg9zqOSETMjI+tmcYTX7yWaZMy+Pz6HXzsPzazv7bN62gSJTrYeQn7atr49xeOcuuSUq7VvHGJM+cPkn5kZTkVBZk8s6+OW7/zMu9ZXMqfv30mi8pyR/ze4SJZrVG8oyK/iK6+Ab7wwHZy04N87baFXscRGTO/z1hbWcCSqbk0dvTx800neWJ3LatnTOKOqnJuWTyZjBTVQLzT0MpF/MNj+zjW2Mm37lhGodZ+lgSQkRLgK++Zx6tfvYGvvGce9W09fPmhXVT947N8fv12ntxTS3ffoNcxZYz0q/gCj2w7w4OvnebP3zGTa2ZrYSxJLDlpQT779pn82XWVbD3ZzH/tqOapvWd5fHct6UE/N8wrJic9yNySbFIC2s+LFyryYV44WM/fPLKbqyoL+Kub5ngdRyRqzIxVFZM4XNfB/NIcjjd28np1Ky8eaqCjd4Cg35hTks3islzmTs5+y7i5xsxji4o8bNfpFv7i59uZU5LNvX+0kqCu/CNJwmfGzKIsZhZl8b6lUzjR2Mme6lb21rSxt6aNgM+YOzmbRVNymTc5m9SgZnDFGhU58PsjjfzZ/dsoyErhx3+6ipy0oNeRRDzhM6OyKIvKcKmfPNcVLvWhYg/6jUVTcplVnMWqinzMzOvIgoqch7ed4SuP7KayKJMf3r1KZ2+KhPnMmFGYyYzCTN67pJST57rYebqF3WdauOPejVQWZnLHqnI+uKJMPzceS9oib+3q52uP7+VX26u5emYB3//ESnLTtScucjHDS/3WxaVkpvr55dbTfP03B/iXpw5y47xiPrqqnLfPKdIFyT2QdEU+MBjisd01/N8nD9DU2cfnrp/Jl26cQ0rApxMhJKGN9fJyF0oJ+PhIVTkfqSrnSH0HD209zSPbz/D0vjpy0gKsmJbPsml5FGenRf3nRz+zQyIqcjN7N/BtwA/c55z7elRTRUFrVz+P7a5h3UvHONXUxcIpOfzo7lWXPcNNRC5vVnEWX71lPl9+11z+14a9bD3RzIuHGnjhUANF2amcae7i2tlFrJyer+mMUTRikZuZH/gecBNwBnjNzDY45/ZFO9yVCIUcB+vaee1EEy8ebOClww30DzqWTM3l725dyTvnl+Dz6UCNyHgI+n0snJLLwim5tHX3s7e2jb3Vrdz70jH+/YWjpAf9LC3PZWl53hsHS2cUZpKmGTDjIpI98tXAEefcMQAzexC4HYhqkYdCjkHnGAwNfQyEHKHwn/2DITp6B2jvGaC9p5/2ngGau/o429pDdUs3Rxs6OFrfSXf/0JlqZXnp3H3mfFZQAAAIUUlEQVR1BbctLWNRWY6OtItEUU56kKsqC7iqsoD3Li1l09FzvHqkkZ2nW/jhK8fpH/zDSoy56UEKslIozEylICuFnLQgfr/hN8PvM3xmOBwDg0M/+wODoaE/w7dPNHYScrzRFSHn+OXW0wyEQoRCkBb0kZ7iJz3oJy047M/wfRkpfjJSA2SEb6en+MlMDbzxtbSgn4BvKEvA53tTtoDPYmZnMJIiLwNOD/v8DLAmGmGWfu1pOnsHGHSOsay6GfAZJTlpVBZlctfqAhZOyWH1jElMzU9XeYt4ICctyM0LJ3PzwsnA0NLQRxs6OFLfwYnGLs519nKuo4/Gjl4O13fQ1t1PaNgO3PkeCPiNgN9HwGcE/EbQ58PvMzp6B/CZ4bOhdWX8PiM7LUDQ78OA3oEQPf2DtHT1090/SE/fIN39g3T1DdI7ELri7TMDvw39wgEoyk7l1a/ccMXPO1qRFPnFGvAtNWtm9wD3hD/tMLODVxJsrI4Cv3/zXYVA41if7+NXFmc8XdF2xBBtR+wY8zZE+nMxQT8/MfNeHAbsq2P+9rlj/cZIivwMUD7s86lAzYUPcs6tA9aNNUi0mNlW51yV1zmulLYjtiTCdiTCNkBibcdYvzeSw8ivAbPNbIaZpQB3AhvG+oIiIjK+Rtwjd84NmNnngacYmn74Q+fc3qgnExGRiEQ0j9w59yTwZJSzREvMDfeMkbYjtiTCdiTCNoC2A9NFWUVE4ptOtRIRiXMJUeRm9m4zO2hmR8zsKxf5eqqZ/SL89c1mVjHxKUcWwXZcZ2bbzWzAzD7sRcZIRLAdf2Vm+8xst5n9zsyme5FzJBFsx2fNbI+Z7TSzV8xsgRc5RzLSdgx73IfNzJlZTM4AieD9uNvMGsLvx04z+7QXOUcSyfthZneEf0b2mtn6EZ/UORfXHwwdgD0KVAIpwC5gwQWP+QvgB+HbdwK/8Dr3GLejAlgC/BT4sNeZr2A7rgcywrf/PI7fj5xht28Dfut17rFsR/hx2cBLwCagyuvcY3w/7gb+zeus47Ads4EdQH748+KRnjcR9sjfWELAOdcHnF9CYLjbgZ+Ebz8M3Gixd6rniNvhnDvhnNsNXPkpadETyXY875zrCn+6iaFzE2JNJNvRNuzTTC5yolwMiOTnA+B/A/8M9ExkuFGIdDtiXSTb8Rnge865ZgDnXP1IT5oIRX6xJQTKLvUY59wA0AoUTEi6yEWyHfFgtNvxKeA3UU00NhFth5l9zsyOMlSCX5ygbKMx4naY2XKg3Dn3+EQGG6VI/119KDxk97CZlV/k616LZDvmAHPM7FUz2xReffayEqHII1lCIKJlBjwWDxkjEfF2mNkngCrgX6KaaGwi2g7n3PecczOBvwH+LuqpRu+y22FmPuBbwF9PWKKxieT9eAyocM4tAZ7lD/8LjyWRbEeAoeGVdwB3AfeZWd7lnjQRijySJQTeeIyZBYBcoGlC0kUuoqUQ4kBE22Fm7wT+FrjNOdc7QdlGY7Tvx4PA+6OaaGxG2o5sYBHwgpmdANYCG2LwgOeI74dz7tywf0v/AaycoGyjEWlfPeqc63fOHQcOMlTsl+b14P84HDwIAMeAGfzh4MHCCx7zOd58sPOXXucey3YMe+yPid2DnZG8H8sZOuAz2+u8V7gds4fdfh+w1evcV/LvKvz4F4jNg52RvB+lw25/ANjkde4xbse7gZ+EbxcyNBRTcNnn9XrDxukv5xbgULgc/jZ83z8wtLcHkAY8BBwBtgCVXmce43asYui3dSdwDtjrdeYxbsezQB2wM/yxwevMY9yObwN7w9vw/OUKMpa344LHxmSRR/h+/L/h92NX+P2Y53XmMW6HAd9k6JoPe4A7R3pOndkpIhLnEmGMXEQkqanIRUTinIpcRCTOqchFROKcilxEJM6pyCXmmdmTI53ZdsHjK8zs9Whmusxrd3jxupLcIrpCkIiXnHO3eJ1BJJZpj1w8Z2b/w8y+GL79LTN7Lnz7RjO738xOmFlheE97v5n9R3id5qfNLD382JVmtsvMNjJ0Ju/5515oZlvC61PvNrPZ4ec5YGY/GbbAUsaw53nRzLaZ2VNmVhq+f6aZ/TZ8/8tmNi98/wwz22hmr5nZ/57gvzoRQEUuseEl4Nrw7Sogy8yCwDXAyxc8djZDS3wuBFqAD4Xv/xHwRefcVRc8/rPAt51zy8LPfSZ8/1xgnRtaYKkN+Ivwa36XoeUPVgI/BP5P+PHrgC+E7/8y8O/h+78NfN85two4O9a/AJEroSKXWLANWGlm2UAvsJGh0r2Wtxb5cefczmHfV2FmuUCec+7F8P0/G/b4jcD/Y2Z/A0x3znWH7z/tnHs1fPt+hn5pzGVoAalnzGwnQ6sZTjWzLOBq4KHw/fcCpeHvfRvwwEVeV2TCaIxcPOec6w+vvPcnwO+B3QxdRWgmsP+Chw9fKXEQSGdobYqLrjXhnFtvZpuBW4Gnwpf/OnaRx7vw8+y9cK/ezHKAlvBe/UVf5rIbKBJl2iOXWPESQ0MWLzG0F/5ZYKeLYDEg51wL0Gpm14Tv+vj5r5lZJXDMOfcdYANDl8oDmGZm5wv7LuAVhpYLLTp/v5kFzWyhG7oS0HEz+0j4fjOzpeHvfZWhFTXf9LoiE0lFLrHiZYaGKzY65+oYuuTYhcMql/MnwPfCBzu7h93/UeD18JDIPIaudwpDe/p/bGa7gUkMjXP3AR8G/snMdjG0quHV4cd/HPhU+P69/OHyXF8CPmdmrzG0zr3IhNPqh5J0zKwCeNw5t8jjKCLjQnvkIiJxTnvkIiJxTnvkIiJxTkUuIhLnVOQiInFORS4iEudU5CIicU5FLiIS5/5/bkaGc9JwmgYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb7a8588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.windspeed,bins=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "形状略微有些左倾，但大体也是符合高斯分布的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "突然发现观察季节和月份的分布是很无脑的，因为一年四季十二个月，难道会因为骑车人数变化而变化？实际上我认为，对于分类特征，尤其是常识可以判断的东西，关注点应该是它们和目标y之间的关联。所以下一步，直接对特征及目标y之间的相关性做计算与可视化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb86cfd0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bm+kuKCtawJqrMf8Znl+L+c8w7eCRP49cwL3S6wBUfb0ID+ISiE7xBfXGazbktbLg/PXU0ypMITQsAudij0dqnZzsiQi/mrpM0ilvvV6PTYECREaaJ09azN0uYmJus9t3n0nnrBplS5HByckh1RSYsLCrhlFMvV5PgQL5n6mOZ8/+jvPnLzHzp19Mntk5ReaIiOvplnnWzBs3bqNOHS/q1Xuf4OCLnD8fYtLcj9y5Gkl+h8ejiAUcbLl7Lf1sgX/tp6x76gvHbp0P5+G9B7xW1jmNrUwnMe/jUcT8DrbcuZb+yGDQXwco4554etrRpTT1h35IL7+puP6vCTU/b0m1zo3NmhcSRz4LOz6+QMzOoTCR1yNTlataqyof9GnP+G5jDaf2M0Pk1VtG0yLsHOyIupY6b6X3qtCqT1smd5+QZt6o61GEnrvCm9UrmDUvJI3UOj5uF9YOtty7+rhdWL6aB5tyzjT+YxitDk6lcLVS1PMeYLioLMeQObemp2naOeBtEju5E4E2QKCmaS5Jj8qapj2ajOYN9EkajR0N5EnaR08SO8HFgACllB1p/u41SN7DiSeX3A7N/3AApUuXpESJYlhaWtKunRfrfbYYlVnvs4WOHT8AoE2bFuzctdewvF07L6ysrChRohilS5d86mnm8Ihr1K1TE4AG9WsRfP6SyV5LRSc7/rl1h7Cou8TGxbP55GXqljP+gnQoaM3BC4kdyYvXY3gYF0+hfK8QFnXXcAFZePRdLt+8jWPBfCbLltzhw8dT1bmPz1ajMj4+Wx/XeesW7NplutPfz8Ic7aJwYVtsbAoAkCdPHho2qM3ZsxfMkj+xjks8zv9By7TruEPi1eatn7GOR40ahE2B/Hw1cJSZMj+u8w8+8EwzcwdD5ubs2rXvqfstUiSxw1mwoA09enRk0SLznHUKP34R25L22BQrgs5STwXPGpzbanxKvFCJx9MsyjRwISok8b1oU6yI4QKyAk6FsXvDgejQG5hTRBp5z289alQmed7SyfIu/2Asc2r1Z06t/hxeuJn9s/7i6GLjv5U5BB8/h0NJR14rVhQLSwtqe9bh0NaDRmVKVnyDXhP7ML7bWGJuxaSzp5fjwvFg7Es6UKTYa+gtLajpWYsjWw8ZlSlRsSTdJ/ZmcrcJ3E6W19beDstXrADIVyAfb7qWI+JCOOZ2K+Ai+Uvaky+pXZTwqkHolsftIvbOPVZX6sXad/qz9p3+3Dx6gV1dfiTyhOm+y4T5ZGrHTinlCERqmrZMKXUX6AEUUUrV1DRtv1LKEiiraVogkB+ISFr2CRCWtI9SmqYdBA4qpTxJ7OT6JpXZoZQqC7wOnAWqvezXmNKgkZPwP3aC6OjbNGzVgd7dOtLG0/y3PomPj6ffl8PZuOFX9Dod3otXEhR0jlEjB3L4yHF8fLaycNEKFnvP4EyQH1FR0XzcIfEWRUFB51i9ej0nj+8kLj6evv2GkZD0a2zZ0lnUrVOTwoVtCbl4mNFjJrPIewU9ew7ixx/HYGFhwYP79+nVa7DJXouFXscQD1d6Ld5BQoKGV7VSlC5akNnbj1PB0Y565Z0Z0PRtxqw7wPJ9Z0ApRreuiVKKY5evs9A3CAu9Dp2CoR5uFMqX5+kHfQ7x8fF8+eW3bPBZjk6vY7H3SoJOn2PkiIEcOZpY54sWrcB70XSCgvyIioymQ8fHt4U6d3Y/BQrkx8rKkpaeTWjR4mNOnwlm4oRhtG/fCmvrvFy84M+iRb8xdtzzXRltjnbh4FCUhb9MQ6/XodPpWL16PRs2bgOgz+f/Y+BXvbG3L8KxI9v4e9MOPus56IXr2Gf9MvR6Pd6LV3L69DlGjPiKo0dO4LNhK4u8V7Bo4TSCAvcQGRlNx06Pb3d29uw+CuRPrGNPzya08PiEO3fuMHRIX86cCebggb8BmDPXm0WLVjx3zrQyr1+/FL1ez2JD5gEcOXKSDRu24u29koULpxEY6EtkZDSdOvVJlnkv+ZNl9vDowJkzwUyZMorKlRNHvCZMmMZ5E/6gTE6LT2DzCG8+WvI1Or2O46t2czM4jDoD2hBx4hLB247i2tmdkrUqkRAbz73b//LXgLkAFHN9k3d7e5IQG4+mJbBp+CLuRd01S87kebeMWEz7JYNReh0nkvLWTsp7fttR3u7sTvFaFUmIjef+7X/ZMGCeWTM9TUJ8AvO/ncuopWPQ6XVsX7mVK+f+4eMBn3D+ZDCHth6i67D/kdc6D4PnJN6q7mb4DcZ3S7y7yoTV3+Fcypk8+fLwy0Fvfho0g2O+R590yBfO6z1iAUOXjESn17Nr1TZCg6/QdsBHXDpxniPb/Pn4my7ksc5Dv9mJ3wW3wm8wufsEnEo702F4VzRNQymFz/x1XDl72WxZH9HiE/AftpiGvya2iwsrdhNzLowqg9oQefySUUc3La0OTsXy1bzorCxwbuLKjo8mERNs/k65yeXQ+9yqlzm/L9XBlWoC/AAkALFALyAOmAHYkNj5nqZp2gKlVC9gMIlTDE4C+TVN66KUWgOUIXG0djvwJfAKMJfEUeE4YICmaTuVUl0AV03T+iQd3weYrGnarvQyxt68mHkV9JzyOtbO7AgZcmfZZ5kdIcNsOs7P7AgZkpCJ7/Pnpddl9qypjDPn3TfM4dui2euzAkCfzep4v/byphmZirXKfic0W8S+mtkRMqxD+LJMb8wPzvmZ/cvhlbK1XvrrzNQWrGnaZmBzGqvqpFF2DjAnjeWp7xYN94EuaZT1JnF6w6PnprlZqBBCCCFEdpNgnguqM1v2GxoRQgghhBAiHdnv3IMQQgghhHhxOXTOrYzcCiGEEEKIHENGboUQQgghcqNMug+tucnIrRBCCCGEyDFk5FYIIYQQIjeSObdCCCGEEEJkbTJyK4QQQgiRG+XQObfSuRVCCCGEyIU0Tf4TByGEEEIIIbI0GbkVQgghhMiN5IIyIYQQQgghsjYZuRVCCCGEyI1y6AVlMnIrhBBCCCFyDBm5zYHuhe/J7AgZltexdmZHyBCV2QEyKLvlBYjPhiMKBfPky+wIGTI1yj+zI2RYgqZldoQMcbC2zewIGRYcHZbZETJsUsVymR0he8qhc26lc/sU2a3TlR07tkIIIYQQpiKdWyGEEEKI3ChB7nMrhBBCCCFEliYjt0IIIYQQuVEOnXMrI7dCCCGEECLHkJFbIYQQQojcKBveleZZyMitEEIIIYTIMWTkVgghhBAiN5I5t0IIIYQQQmRtMnIrhBBCCJEbyZxbIYQQQgghsjYZuRVCCCGEyI1k5FYIIYQQQoisTUZuhRBCCCFyIU2Lz+wIZiGdWyGEEEKI3EimJYiUmrjXI/CUL2eC/Bg86PNU662srPh1+RzOBPmxz289xYs7G9Z9PbgPZ4L8CDzli3vjuoblC+ZPITz0OAHHthvtq2rViuzds57D/ls4sH8jbq4u5nthKQyf8CN1WnxIqw49X9oxH8nudezuXo9Tp3w5HeTHoHTyL18+h9NBfuxNkX/w4D6cDvLj1ClfGifL36/vpwQE7ODYse0sXTqLV155JctlDD53gGNHtxnq8pFvvx1AyKXDHPbfwmH/LTRt2uCFsoPp24izsyPbtvzOyRO7OB6wgy/6dHvhjCk1aFSbA0c2cShgK33790gjsyU/L5rGoYCtbN7xO8VedzJa7+TsQEj4MT7/4n+GZT16dWLPAR/8Dm7gs96ds3zenp93we/gBvYc8GH+wh955RUrk2Zu2Kg2B49u5nDANvoNSCuzFb94T+NwwDa27lidZuZ/IgLo0/fx37+ATX68l87kwJFNHDi8Cbfq5vscrlW/Bj57V/H3gdV0/6JTqvVv13Dh962LOR62F3cP4/fRvN+msf/cNmYtm2K2fJD02XFyN0FBfgwamM5nx7LZBAX54bfn8XvP1rYgWzavIvLWWaZNG2conzdvHtauXczJE7sIOLad8eOGmjV/nnfdcFyzCMd1iynQ5cNU6/N5uuO8fTUOv83F4be5vNqqGQCvuFY1LHP4bS6v799I3nrvmjWryBjp3D4nnU7HjOnj8fDsQOWq9WnfvhXly5cxKvO/rh8RFRVDuQq1mDZjARMnDAOgfPkytGvnRRWXBrTw+ISZMyag0yX+KZYsWUULj09SHW/ShGGMHfcjrm7ujB49mUkTh5n/RSZp1bwxc38c9/SCJpbd6/hRfk/PDlSpWp8P08kfHRVD+Qq1mD5jAROS5W/fzouqLg3wSJbf0dGezz//HzVqNOettxqi1+tp384rS2V8pFHjD3B1c6dGzeZG+5s+YwGubu64urmzadOO586ePL8p20hcXByDBo+mcpV6vFfLk169uqTa54tm/m7KSNq3+ZT33JrTuq0HZd8sZVTmk04fEB0dQ3WXxsyd5c3I0YOM1o+b+A3bt/oanpcrX4aOndvhXr8tdd9tiXuT+rxRqniWzWvvUJRPP+tIo7qtqV3DA51Ox/ttWpgk76PM308ZRbvW3anp1ow2bT14883SRmU6dGpLdPRtXF0aMWfWIkaNMc48YdIwo8wAE78fzvZtvtR4uym1a3py9uwFk2VOmX/YpEH0/PhLWtb+kObvu1OqbEmjMhFh1xjWbywb1mxJtf3C2csY2meUWbIlzzh9+jg8W3akatX6tG/vRflyxu+Trl0/JCo6hgoVajFjxgImjP8GgPv3HzBq9A98PWRsqv1OnTqPylXq4Va9KTVrutKkSX1zvQBsv/6C6198Q3ibbuRrWh/Lkq+nKvbvll1EfNSTiI96cnft3wA8OHzcsOzaZ4NIuH+f+weOmCenuWkJ5n9kglzduVVK6Z932+pub3HhQgiXLv1DbGwsq1ato6VnE6MyLT3dWbr0dwD++GMDDerXSlrehFWr1vHw4UNCQq5w4UII1d3eAmCP30Eio6JTHU/TNPIXyA8kjh6ER1x73ugZ5upSGZukY79M2b2OU+ZfuWodninye6aT39OzCSvTyW9hYUHevHnQ6/VY581LeMTVLJfxZTFHG7l69TrHAk4BcPfuv5w5E4yTo73JMldzrcKli5e5HHKF2NhY/vxjA81aNDIq06xFQ1b89icAf63dRO16NZOta8TlkCucPXPesKzsm6U44n+ce/fuEx8fz769h2jh0TjL5oXEdpznUTu2zsvVq9dNkhfg7RSZ1/yxgWYeDY3KNG/RiBW/rgFg3dpN1EmWublHI0JCrnDmdLBhWf78r/Luu24sXZzYlmJjY7kdc8dkmZOrXK0CVy6FEno5nNjYODau3Ur9pnWMyoRfieBc0Hm0NE4rH9xzmH/v/meWbI+4ubmkeu95eroblTH67FizgfpJ773//rvHvn3+3L//wKj8vXv32b17H5BYv8cCTuHk5GCW/FaV3iQuNJy4sAiIi+PfzbvIW++9DO/HulEd7u/1R0vxWkTmytGdW6XUWKVUv2TPxyul+iqldiqlfgVOPu++HZ3suRIabngeGhaBY4ovwORl4uPjiYm5jZ1dIRwd09jW6clfngMGjuS7icO5dMGf7yd9y7DhE583eraR3evY0cme0GQZwsIiUnWS0svv5Jh6W0cne8LDrzJ16lwuXjjElX+Ocfv2bbZtMx5dyuyMkPhD4e+Nv3HwwN9072Y8St67V1eOHtnKgvlTKFjQ5rmzp8wGpm8jxYs741K1EgcPHXuhnMk5OBQlPPTxD5Lw8Ks4OBZNVSYsNMKQ+fbtO9jaFsLaOi99+3/KD5N+Mip/OiiYmu+5Usi2IHnz5qGRe10cnU3TKTBH3qsR15g18xcCAncRGLyX27fvsGvHXpPkTcxjT1hYxOPMYVdxcEiR2bEoYUmvKz4+ntsxd7G1S8zcr38Pvp8406h88RLFuHkzkp/mfscuv3VM/2k81tZ5TZY5uaL2rxER/vjH9bXw6xS1L2KWYz0vJ0cHQq88ruOwsKs4puiIJn5GPG4XMbcT33vPwsamAC1aNGLnTj/ThU7Gokhh4pL9oIq/fgP9a3apylk3qI3DyvkU/n4E+qKp/wb5mtTj380vdgYqUyUkmP+RCXJ05xb4BegMoJTSAR8CYUB1YJimaRWed8dKqVTLNE17hjLPtm1Kn/XoxFeDRlGylBtfDRrNgnnmnUuVFWT3OjZH/oIFbfD0bEKZsjVWU0QrAAAgAElEQVR4vXg1rPNZ8/HHrbNURoC69VpR/Z2meHh2oFevLtSq9Q4A8+Yt4c1y7/K2qzsRV6/zw/cjnju7OfMD5MtnzaqVCxgwcCR37tx9oZxPz/MMmdH4+pu+zJ3lzb//Go/KBZ+7wIypC/hj7SJWrfmFwJNniI+Ly7J5bQoWoFnzhrxduQGVytbC2tqaD9q3NEnexDyplz1bu9AYMqwvc35alCqzhYWeqi4VWfTzr9Sr5cV//97jywGfmSyzcbjUizSe/Pn1sr1IHT+NXq9n6dJZzJq1kEuX/nnujE+U5gswfnrP9wBhHh2IaN+D+wePUnjMYOOchW2xLF2Se/sPmyejeG45unOraVoIcEsp9RbgDhwDbgGHNE27lN52SqkeSqnDSqnDCQn/plkmLDSCYs6OhufOTg5EpDiNnbyMXq/HxqYAkZFRhIWlsW34k0+Bd+r4AX/+mXhhzurV63Fze3kXlGWW7F7HYaEROCfL4OTkkGqqQ3r5Q8NSbxsRfo2GDWsTEvIPN29GEhcXx9q1f1OzhmuWyggY/k43btxi7bq/DXV5/fpNEhIS0DSNX35ZjqsJ6tgcbcTCwoLfVy7gt9/+ZG3SPDtTCQ+/iqPz4xFiR0d7rkZcT1XGKWnkVa/XU6BAfqIio6nmWpWRYwZx9OQOPuvVmS8H9qRbjw4ALF+6mgZ13sez2SdERcVw4cLlLJu3br13uXw5lFu3ooiLi8Nn/Rbc3jHdlJbw8KtGp7MdnexTTXsID7uKU9Lr0uv1FLB5lajIaN52rcqosYMJOLWTnr270P+rnnTv0YHwsKuEh13lyOHjAKxbt4kqLhVNljm5axHXjUbHizq+xvWrN81yrOcVGhaBc7HHdezkZE9E+NXUZZK1C5sCBYiMTD0lLKU5s7/j/PlLzJz5i2lDJxN3/QYW9q8ZnutfK0L8jVtGZRJibkNsLAB3/9yIVbmyRuutG9flv517IS4b305L5txmWz8DXYCuwMKkZWn3WJNomjZf0zRXTdNcdbp8aZbxPxxA6dIlKVGiGJaWlrRr58V6H+OJ/et9ttCx4wcAtGnTgp279hqWt2vnhZWVFSVKFKN06ZIc8n/yac/wiGvUrZM4J6xB/VoEn0+3b55jZPc6Tpm/fTsvfFLk90knv4/PFtqnkf/KP2FUf6caefPmMeQ8cyaY52WOjNbWeXn11cT3jbV1Xho3qktg4FkA7JN9mbTyamZYbqr8pmojC+ZP4fSZ80ybPv+F8qXl2JGTvPFGCV4v7oylpSXvt2nBpo3Gd+7YtHEHH370PgAtWzVlz+79AHg2/ZhqlRtQrXID5s1ZzLTJc/ll/jIAChe2BRKv8vdo6c6a1T5ZNm9oaDiubi6Gdlynbk3Onb1okrwAR4+c5I1SjzO3btOCTRuMM/+9cTsfJp318GrVlD27DwDQosnHuFSqj0ul+syd7c3UKXP5ef4yrl+/SVhYBKXLJF7YVbduzVTziE3l1LHTvP5GMZxed8DS0oLmrRqzc/PzTz8yh8OHj6d67/n4bDUq4+Oz9fF7r3ULdu16+tST0aMGYWNTgK++GmmW3I88DDyLRTEnLBztwcKCfE3qcS9pvu8j+qT3FEDeujWJDTEeRc7XtAH/vuBFscI8csN9bv8ExgCWwMdAbVPsND4+nn5fDmfjhl/R63R4L15JUNA5Ro0cyOEjx/Hx2crCRStY7D2DM0F+REVF83GH3gAEBZ1j9er1nDy+k7j4ePr2G0ZC0ryUZUtnUbdOTQoXtiXk4mFGj5nMIu8V9Ow5iB9/HIOFhQUP7t+nV6/BT4pnUoNGTsL/2Amio2/TsFUHenfrSJsUF+2YQ3av40f5N6TIP3LkQI4ky+/tPYPTSfk/SZb/99XrOZEi/yH/Y6xZs4FDhzYTFxfH8YBAFvy8PEtlLFq0CKt/Txxx0VvoWbFiLVu27AJg0sThVK1aAU3TCLkcSu/eX5ukjk3ZRt57142OHdpy4mQQh/0TO8rffjuJv030JRYfH8+QQWP4/c9f0On1/Lp0NWfPnGfIsL4EHD3Fpr93sHzJ78ye/wOHArYSHRXDp137P3W/i5b9hK1tQWJj4xj81Whiom9n2bxHD59g/brN7Nizlri4OE6eOM2SRStMkvdR5sEDR7N67UL0Oj3Ll67mzJnzDB3Wj2PHTrJp4w6WLfmduQsmczhgG1FR0XR/hjr+euBY5v08BSsrS0JCrtCn1xCTZU6Zf/zQycxfMQOdXsefv63nwtlL9Bncg8Djp9m5eQ+VXMozfdH3FCiYn3rutfl80Kd41f0IgCXr5lGydHGs8+Vl+7H1jOg/jr27Dpo845dffssGn+Xo9DoWe68k6PQ5Ro4YyJGjie+9RYtW4L1oOkFBfkRFRtOhY2/D9ufO7qdAgfxYWVnS0rMJLVp8zO07dxk6tB9nzgRz6OAmAGbP8WbRot9Mmj3xBSQQ+d1MXps1CXQ67v61idiLl7Hp2ZmHQee457uf/B++T966NSE+noSYO9wc+b1hc71DUfRFi/DgyAnTZ3uZcuh9btWzzH/J7pRSc4FoTdOGKKXqAQM1TfN4lm0trJyyVQXdC9+T2RGeS15Hk/zmeGnSmK0lTCxbvfGSFMyT9pkeYToJ2ew7y8Ha9umFspjg6LDMjpBhwRXLZXaEDCt+dFumf5Xc2zLb7G+ovO69X/rrzPEjt0kXktUAPgDQNG0XsCsTIwkhhBBCZL5MmhNrbjl6zq1SqgJwHtiuadrzT0wUQgghhBDZQo4eudU0LQh4I7NzCCGEEEJkOTl0zm2OHrkVQgghhBC5S44euRVCCCGEEOmQkVshhBBCCCGyNhm5FUIIIYTIjeRuCUIIIYQQQmRtMnIrhBBCCJEbyZxbIYQQQgghsjYZuRVCCCGEyI1kzq0QQgghhBBZm4zcCiGEEELkRjl0zq10boUQQgghciOZliCEEEIIIUTWJiO3QgghhBC5kUxLyJ3uLPsssyPkCvfC92R2hAzL71wvsyM8s4TseOpJ0zI7QYYlZLPMDW0rZHaEDLNAZXaEDIkje7UJgLJFX8vsCBk2JdI6syNk2IzMDpCDSec2h8nrWDuzI2RYduzYCiGEENleDh25lTm3QgghhBAix5CRWyGEEEKI3CibTaV6VjJyK4QQQgghcgwZuRVCCCGEyI1kzq0QQgghhBBZm4zcCiGEEELkRjJyK4QQQgghRNYmI7dCCCGEELlRdvwPfp6BjNwKIYQQQogcQ0ZuhRBCCCFyI5lzK4QQQgghRNYmnVshhBBCiNxI08z/eAqlVFOl1Fml1Hml1JA01r+ulNqplDqmlDqhlGr+tH1K51YIIYQQQrx0Sik9MAtoBlQAPlJKVUhRbDiwStO0t4APgdlP26/MuRVCCCGEyI0yf85tdeC8pmkXAZRSKwAvIChZGQ0okPRvGyD8aTuVkVsz2Bscjte0v/Ccuo6FvoGp1kdE/0v3hdtoP2sjH/y0gT3nwgA4GXqTdrM2Jj5+2sCOoCsmzdXEvR6Bp3w5E+TH4EGfp1pvZWXFr8vncCbIj31+6yle3Nmw7uvBfTgT5EfgKV/cG9c1LF8wfwrhoccJOLbdaF9Vq1Zk7571HPbfwoH9G3FzdTHpa3ma4RN+pE6LD2nVoedLPW5KjRvX5cSJnQQG+jJwYO9U662srFi6dBaBgb74+q4z1LmtbUE2b17BzZunmTp1jNE2bdt64u+/maNHtzF+/DcvnNHdvR6nTu4mKMiPQQPTbhfLl80mKMgPvz3G7WLwoM8JCvLj1MndNE7WLvr06caxo9sIOLadL77oZlj+7fABXLp4GP9Dm/E/tJmmTRs8f+ZTvpwO8mNQOm15+fI5nA7yY2+Ktjx4cB9OB/lx6pSvUebgcwc4dnSboc0+UqVKBfb4/sWxo9v4809v8ud/9bkyJ9ewUW0OHt3M4YBt9BvQI838v3hP43DANrbuWE2x152M1js5O/BPRAB9+nYzWq7T6djlt47ffp//whnT41L3LabvmM3M3XNp1atNqvUe3VsyddtPTN40nRG/jqGwUxEASlQoyfg/v+PHrTOZvGk673rUMlvGlKrWfYspO2YxdfccWvZqnWp98+4t+WHbTL7bNI1hyTIXdirCeJ8pTNw4lR+2zqDRJ01eWubsVs9v1a3GTzvnMNt3Hq17t021vmV3L2Zsn8XUzTMY/ds4iiTlBfh2ySiWnfyNYYtGvJSsj5SvW5Vh26fy7a7pNOrllWp9/W4t+GbrFL7++3s+Xz6cQk6FDetaDvmEoVsm8822H2kzsstLTJ39KKV6KKUOJ3sk/9BzApJ3dkKTliU3CuiglAoFNgJfPO2YObpzq5QqqJTqnex5PaWUjzmPGZ+QwMT1/szqVJ81X3iw6UQIF67HGJVZsPsU7pVeZ+XnzZnUrhYT1vsDUPq1gvzasymrPm/OrM4NGPvXQeLiTfOrSqfTMWP6eDw8O1C5an3at29F+fJljMr8r+tHREXFUK5CLabNWMDECcMAKF++DO3aeVHFpQEtPD5h5owJ6HSJTWfJklW08Pgk1fEmTRjG2HE/4urmzujRk5k0cZhJXsezatW8MXN/HPdSj5mSTqdj+vRxeHl1xsWlIe3ataRcOeM679KlPdHRMVSsWIeZM39m3LihANy//4DRo6cwZMh4o/K2tgWZOPEbmjX7iGrVGlG0aGHq13/vhTN6tuxI1ar1ad/ei/IpMnbt+iFR0TFUqFCLGTMWMCGpQ12+XGK7cHFpgIdnB2bMGI9Op6NihTfp9r+PePc9D952dad580aULl3SsL8ZMxfgVr0JbtWbsGnTjufKPGP6eDw9O1Clan0+TKctR0fFUL5CLabPWMCEZG25fTsvqro0wCNFWwZo1PgDXN3cqVHz8ZSueXN/4JthE3irWiPWrf2br77qleHMKfN/P2UU7Vp3p6ZbM9q09eDNN0sblenQqS3R0bdxdWnEnFmLGDVmkNH6CZOGsX2rb6p99+zdmXNnL7xQvqdl7zb2M8Z3Hk3/Rn14r2VtnMsUMypzKfASX3sMYGDTfhzYuI+OQ7sA8ODeA2b2n8aAxl8wvtNouozshnWBfGbL+ojS6eg69jO+6zyGgY2+4N2WtXEq42xUJiTwIsM8vuLrpl9ycOM+Ph7aGYCo61GMbP01Q5v3Z7jXYFr2akOh1wqZPXN2q2edTkePcT0Z23kUfRt+Tq2WdVLlvRh4kYEtBtC/SV/2bdhLp2+6GtatnbeGaf1/NGvGlJRO8cGY/zG3y0QmNB7A2y3fw760cZ8qNCiEHzyH8l2zwRz/+yBeQxO/60pWK8sbrm8yqekgJrp/xetVS1G6Rsoz6dlEQoLZH5qmzdc0zTXZI/mvb5VGqpQTdT8CvDVNcwaaA0uVUk/sv+bozi1QEEg9XGZGp0JvUcwuP862+bG00NOkcnF2nTYegVXAv/djAbh7/yFF8ucFIK+VBRb6xD/Jw7h4VJp/8+dT3e0tLlwI4dKlf4iNjWXVqnW09DQehWjp6c7Spb8D8McfG2hQv1bS8iasWrWOhw8fEhJyhQsXQqju9hYAe/wOEhkVnep4mqaRv0B+AArY5Cc84prJXsuzcHWpjE3S8TOLm5uLUZ3//vt6PD3djcp4erqzbNlqANas2WjoqP733z327fPnwYP7RuVLlnyd4OBL3LwZCcCOHX60atXMZBlXrVqXZkZDu1izgfpJ7cLT0z1Vu3Bzc6FcudIcPHiMe/fuEx8fzx7fA3h5NX3ujCmlbMsrV63DM0Vb9kynLXt6NmFlOm05PWXLlmLPngMAbNu+h/fff+q1DE/0tmsVLl28zOWQK8TGxrLmjw0082hoVKZ5i0as+HUNAOvWbqJOvZqP13k0IiTkCmdOBxtt4+hoT+Mm9Vi6eNUL5XuS0i5luBpyletXrhEXG8fe9XtwbVzdqEzg/pM8vP8QgHPHzmLrYAdAxKVwroZEABB1PZKYmzEUsC2AuSVmjuD6lWvEx8axf70fro3fMSoTtP+UIfP5ZJnjY+OIexgHgKWVJUpnus/kp2fOPvVcxqUMESERXPsnMa/fel+quxvX8an9J3l4/4Ehr11SXoCTe09w7+49s2ZMqbhLaW5cvsatK9eJj43n6Pp9VHZ3MyoTvD+Q2KQ6DjkWTEH7xMwaGpavWGJhaYGFlSV6Cz13bsSkOka2oCWY//FkoUDyX0LOpJ520A1YBaBp2n4gD1CYJ8jynVulVAml1Bml1M9KqVNKqeVKqUZKqb1KqWClVHWl1Cil1EKl1C6l1EWlVN+kzScBpZRSAUqpH5KWvaqUWp20z+VKKZN+Wl2/fQ97G2vD86I21ly/Y/ym7dmgChuOX8L9hzX0WbqLIS1cDetOXrlJ6xk+tP1pA8NbVjd0dl+Uo5M9V0Ift5fQsAgcHe3TLRMfH09MzG3s7Arh6JjGtk7G26Y0YOBIvps4nEsX/Pl+0rcMGz7RJK8jO3F0tCc0Wb2FhUXg6Fg03TLx8fHcvn0HO7v0R4YuXLhM2bKlKF7cGb1ej6enO87Ojs+d0cnRgdArEckyXsXRySFFGXtCQyMMGWNuJ7ULJwfDcoCw0Ks4OToQGHSW2rXfwda2IHnz5qFp0wZGGXv17MKRw1uZP28yBQvaZDizo1PqenV6xrbslNbfJKkta5rG3xt/4+CBv+ne7fHZiMDAs4YOf9s2HhR7gfoGcHCwJyzscb2Fh13FwcG4XTg4FiUs9Koh/+2Yu9jaFcLaOi/9+vfg+4kzU+13wnfDGPXt9ySYcQ6drb0dtyJuGp5HRtzCzt4u3fIN2zfm2K4jqZaXrloGCysLrl2+apacyRWytzXKfCviFoXsbdMtX699I47vOmp4butQmO82TeOnAz/z19w1RF2PMmteyH71bGtvx81w4zq2K5p+3kbtG3N0Z+q8L1PBorZEh98yPI+OuIVN0fQ/e2u0q0/QrgAAQo4Gc25/IGP95zHu0DxO+x7n2oUws2fOofyBMkqpkkopKxIvGPsrRZl/gIYASqnyJHZubzxpp1m+c5ukNDAdqAKUAz4GagEDgUeTDssBTUicnDxSKWUJDAEuaJrmomnao/N6bwFfknhV3htAqnO6yeeH/LLtcIaCaqlG01OPuW86EULLaqXYMqg1P3Wsx/A/9pGQkLhd5WKFWdPXg+WfNeUX30AexMZn6PjpSasPr6W4RUfaZZ5t25Q+69GJrwaNomQpN74aNJoF86ZkMHH29/x1nn7dRkfH0LfvMJYuncX27au5fDmUuLi4F8iYetmzZkxv2zNnzvPD5Nn8vfE3fNYv48TJIEPGefOXUK78e7i6uXP16nW+/+7b58hsnrZct14rqr/TFA/PDvTq1YVatRJHnj7tMYBePbtw8MDfvJo/Hw8fxmY4s3G21Muetc6HDOvLnJ8W8e+//xmtc29anxs3bnE8IPUcf3NLr73Wfr8ub1QuzV/z/jRaXvC1QnwxtT+zB8546ueIKaR5Biydw9ZKyrw+WebIiJt83fRL+tfpSZ029bEpnPEfZKaQles5I59jdd+vR6kqpVk7b41ZMz1VOp8RaXFtVYvXq5Rix/zEPlfh4kWxL+3EiBq9+LZGT8q+W4lS1cubM63ZaAma2R9PPL6mxQF9gM3AaRLvihColBqjlGqZVOwr4FOl1HHgN6CL9pRGnV06t5c0TTupaVoCEAhsT3phJ4ESSWU2aJr2QNO0m8B1oGjau+KQpmmhSfsKSLa9QfL5Id0auabawZMULWDN1ZjHXzzXYv4zTDt45M8jF3Cv9DoAVV8vwoO4BKL/e2BU5o3XbMhrZcH566lP+T+PsNAIoxEnZycHIlJMFUheRq/XY2NTgMjIKMLC0tg2/MnTDDp1/IA//0y8KGf16vW4ub3cC8qygrCwCKMRSycnByIirqdbRq/XU6BAfiIjn/w337hxG3XqeFGv3vsEB1/k/PmQ584YGhaBc7HHI7VOTvZEhF9NXcbZwZDRpkABIiOjCQt9vBzAydme8IjEbb29V/BOjWY0bNSWqMhozp+/BMD16zdJSJyDxS8Lf32udpF4XON6TTntJb22HJrW3ySpLT96P9y4cYu16/42ZDt79gLNW3zMOzWasXLlOi5eDMlw5uTCw6/ilGx03NHJnqtXjdtFeNhVnJztDfkL2LxKVGQ0b7tWZdTYwQSc2knP3l3o/1VPuvfowDs1qtGseUMCTu3kZ+9p1K5Tg7kLJr9QzrREXr2FncPjs4G2DnZEXotMVa7ye1Vp3ecDvus+3nBaHyDvq3kZuuhbfpu8jOBj50ye71ky2znYEZVG5krvVaFVn7ZM7j7BKPMjUdejCD13hTerm39uZXar51sRNynsaFzHkddT561Sqypt+7RjYrdxadbxyxR99RYFHR+PLhd0sON2GqPyZd+rjHuf1szv/r0hc5Um1Qk5FszD/x7w8L8HnN4VQIm3yqTaVjwbTdM2appWVtO0UpqmjU9aNkLTtL+S/h2kadp7mqZVTRqs3PK0fWaXzm3ynl9CsucJPL6dWfIy8aR/m7NnLfdcKjrZ8c+tO4RF3SU2Lp7NJy9Tt5zxxQsOBa05eCGxE3DxegwP4+IplO8VwqLuGi4gC4++y+Wbt3EsaJoLAfwPB1C6dElKlCiGpaUl7dp5sd7HuH2s99lCx44fANCmTQt27tprWN6unRdWVlaUKFGM0qVLcsj/2BOPFx5xjbp1EucJNqhfi+Ckzk1ucvjwcaM6/+ADT3x8thqV8fHZSocOiVcWt27dnF279j11v0WKJH4gFyxoQ48eHVm06DeTZWzXzivNjIZ20boFu5LahY/P1lTtwt8/wChjsWKOtGqV2CkEsLd/zbBfL6+mBAaezXDmlG25fTsvfFK0ZZ902rKPzxbap9GWra3z8uqrie81a+u8NG5U15Dt0WtRSvHN0H7Mn780w5mTO3rkJG+UKsHrxZ2xtLSkdZsWbNpgfLeRvzdu58OPE6/q92rVlD27E+f8tmjyMS6V6uNSqT5zZ3szdcpcfp6/jLGjplCpXG1cKtWne5cv2eN7gJ6fDnyhnGk5fzwYh5IOvFbsNSwsLXjPszaHtx4yKlOiYkl6TOzFd93Gc/vW43mIFpYWDJo/lN1/7OTAxqe3c1O5cDwY+5IOFCn2GnpLC2p61uJIGpm7T+zN5G4TjDLb2tth+YoVAPkK5ONN13JEXHjqXYheWHar5+DjwTiUdOS1YkWxsLSglmcd/FPkLVnxDXpN/JwJ3cYScyvz56f+c/wCRUrYY+tcBL2lnmqe73Jyq/GZWueKJfhwQncWdP+eu7duG5ZHhd+k9DsV0Ol16Cz0lHqnPNfOh77sl2AaL+GCssyQ0+9zewd4qVcVWeh1DPFwpdfiHSQkaHhVK0XpogWZvf04FRztqFfemQFN32bMugMs33cGlGJ065oopTh2+ToLfYOw0OvQKRjq4UahfHlMkis+Pp5+Xw5n44Zf0et0eC9eSVDQOUaNHMjhI8fx8dnKwkUrWOw9gzNBfkRFRfNxh8Rr8YKCzrF69XpOHt9JXHw8ffsNM8zrW7Z0FnXr1KRwYVtCLh5m9JjJLPJeQc+eg/jxxzFYWFjw4P59evUabJLX8awGjZyE/7ETREffpmGrDvTu1pE2ni/vNj6QWOdffvkt69cvRa/Xs3jxSk6fPseIEQM4cuQkGzZsxdt7JQsXTiMw0JfIyGg6depj2P7s2b3kz58fKytLPD2b4OHRgTNngpkyZRSVKyeOHk2YMM0wKvoiGTf4LEen17HYeyVBp88xcsRAjhxNbBeLFq3Ae9F0goL8iIqMpkPHpHZxOrFdHD++g/i4ePr1G25oFytXzMfOrhCxsXH07TeM6OjEL7OJE4ZRtWpFNE3j8uUr9P481X9G80yZ+305nA0p2vLIkQM5kqwte3vP4HRSW/4kWVv+ffV6TqRoy0WLFmH1778AoLfQs2LFWrZs2QXAh+1b0bNXFwDWrt2I9+KVz13fj/IPHjia1WsXotfpWb50NWfOnGfosH4cO3aSTRt3sGzJ78xdMJnDAduIioqme9f+L3RMU0mIT+CXEfMZtmQUOr2Onau2Exp8hfYDPubCifMc3naIjt90JY91Xr6anfievxl+k++6j6emx3uUr16R/AXzU79t4i3gZg2cQUiQeX/4JsQn4D1iAUOXjESn17Nr1TZCg6/QdsBHXDpxniPb/Pn4my7ksc5Dv6TMt8JvMLn7BJxKO9NheNekaTgKn/nruHL2slnzPsqcneo5IT6BBd/OZeTS0ej0Orav3MaVc//w0YBPOH8yGP+th+g8rCt5rPMwaE7ie/5G+A0mdku8o8341ZNwKuVMnnx5WHBwEbMGzSDA98kDKKbIvHrEQnov+QadXseBVbu4GhxK8/4f8M/Ji5zadgSvoR2wss5D19mJ77+osJss+PQHAjYeoOy7lRiyeTJoGqd3B3Bq+9GnHFG8TOplzHl6EUqpEoCPpmmVkp57Jz1f/WgdsBq4q2na5KQypwAPTdNClFK/kjhX929gAzBQ0zSPpHI/AYc1TfNO7/j3Vo3J2hWUQv4O8zI7QobdC9+T2RGeS37nepkd4ZklPP2K1Swnq382pSX/K9ZPL5SFNLTNfrcvsjDhXWRehrj0JvhmYXHZ8POimC57vfcAZoSszPTG/N+cL8zeQK17zXzprzPLj9xqmhYCVEr2vEt665ItT17+4xSrdyVb1wchhBBCCJFjZPnOrRBCCCGEMIOn3M0gu8ouF5QJIYQQQgjxVDJyK4QQQgiRG2XS3QzMTUZuhRBCCCFEjiEjt0IIIYQQuZGM3AohhBBCCJG1ycitEEIIIURulA3vJ/4sZORWCCGEEELkGDJyK4QQQgiRG8mcWyGEEEIIIbI2GbkVQgghhMiN5H8oE0IIIYQQImuTkVshhBBCiNxIy5lzbqVzK4QQQgiRG+XQaQnSuX0Km47zMztChqjMDpCL3AndlQjyuIcAACAASURBVNkRMqRyhfaZHSFD3rUuntkRMmz1jaOZHSFD/r5xIrMjZJhOZa9PuYfxcZkdIcPiE+IzO0KGvZavYGZHyLAZmR0gB5POrch0+Z3rZXaEDMtuHVshhBAiJU1uBSaEEEIIIUTWJiO3QgghhBC5UQ6dcysjt0IIIYQQIseQkVshhBBCiNwoh94KTEZuhRBCCCFEjiEjt0IIIYQQuZHMuRVCCCGEECJrk5FbIYQQQojcSO5zK4QQQgghRNYmI7dCCCGEELmRzLkVQgghhBAia5ORWyGEEEKI3EjucyuEEEIIIUTWJiO3QgghhBC5kcy5FUIIIYQQImuTzq2JuLvX49TJ3QQF+TFo4Oep1ltZWbF82WyCgvzw27Oe4sWdAbC1LciWzauIvHWWadPGGW0zZvRgLpw/ROSts+bLfMqX00F+DBqUTublczgd5Mdev8eZAQYP7sPpID9OnfKlceO6huX9+n5KQMAOjh3bztKls3jllVdMlrdx47qcOLGTwEBfBg7snWbepUtnERjoi6/vOqM63rx5BTdvnmbq1DFG27Rt64m//2aOHt3G+PHfmCxrRg2f8CN1WnxIqw49My3D09SqX5O/961m88E1fPpF51TrXWu8xR/blnIqfD9NPBpkQkKoVNeFCdunM3HXTJr3apVqvXs3D8Ztncr/2bvvqCiuv4/j78sKlthNghR7iSWKBayogIoiINgwiZpoiokpxh6NPTHGJE80Yoq9G8VeKIqKClhpIgI2FJWmUQH9WWGZ54/FdZeiKItY7uscjruz35n97Dh753L37jDd73fGrJlKFYs3tY8tjvNimu9vTPP9jW8WfVekObt07UhYxB6Onwhg5Ojc/+cmJiYsW+HJ8RMBBOzfTPXqFgC0bNmU4MPeBB/25uARH1xcHbXrRMUEcviYH8GHvdkftM3gecOP7yUyah+j8sm7YuU8IqP2se/AFm1eewdbgg5u5+gxP4IObqdTp7badfr0cebIUT9CQnfx44zxBs37MLMh93HJkibsO7CFg0d8OBqyk+8njjB45qJo44yNjfnrr1lERe0nMjIAd3enQmUsivNGhQrlWbduIVFRBzhxYj9tWrcEYNq0sYSH7SY0xB9fn38xMzMtVHYAu862BB7zJjjMj69GfJpHfmP+WfJ/BIf5sWP3WiyrmWsfa9i4Ptt3rSHg0Db2HNxCyZImALj16cGeg1vYHbyZ1RsWUKlyxULnfF6UrKwi/ykOxda5FULUFEKcfIr65UKIvtm3FwshGuVRM1gI8achcxaEkZERc+fOwLXnIKys7Onf342GDerp1QwZ8h6paek0amSLp+ciZmZ3pO7du8+06b/x3fgfc23X22cP7W1diiyz59yfcHUdSFMre97r707DhvqZPx7yPmmp6TRsZMtcz0XMnDkRgIYN69Hfww2rZg64uAxgnudMjIyMMDevyldffUybNj1o3rwzKpWK/h5uBss7d+4M3Nw+olmzznh49KRBjn08eHB/0tLSady4I/PmLWbGjAmAZh9Pn/4748f/pFdfuXJFfv75e5yc3qdFiy6Ymr6JvX17g+R9Wu49ujJ/9ownFxYTIyMjpvwyjs/e/xYXWw+ceztSp34tvZrkxBQmDJ+O9+ZdxZJRGBkx8IdPmTP4JyZ1HUnrnraY17XUq7kUc4EfXL9jqtNoQv0O02/CIO1jD+49YFqPsUzrMZZ5n/1SZDmNjIz4ffZ0+vQagk3LbvTt58o7Derq1Xz4kQdpaTdp1tSBv/5cyvQfNZ3tmJgzdLJ1w7atC73dBzN33gxUKpV2PWenD7Bt64JdB8O87x7mnT3nB3q7D8a6hSP9+vWkQY68Hw32IC0tHasm9vw1b4m2s3r9+g369f2U1q2c+PyzMSxaMhvQvPdmzJyAi/MAbKy78fbbb2Jn186gmQ29j+/ff4BLjwG0b+NM+7YudOnaERubZgbNbOg2DmD8+G/4779rNGliR7NmnQkKOlKojIY+bwDMmf0D/rv20aRJJ1q27ErsqbMA/P77P7Ro2RVrG0d8ffcwaeLIZ87+MP9Pv01kYL8vsG/TE/c+Paj3Th29mvcH9SE9/Sa2LZ1Y9M9KJk4bBYBKpcJzwSzGj/4Bh3Zu9HMZTEZGJiqVih9+Hk8/1yF0te1NbMwZhnz2QaFySoX3Uo7cKoryqaIoMcWd4yEbm2bExcVz4cIlMjIyWL9+G646IyoArq6OrFq1AYBNm32wt7cF4M6duxw6FMK9e/dzbffYsXBSUq4WSeZWNs31Mnut34ara7f8M2/ywSE7s6trN7zWb+PBgwfEx18mLi6eVjbNAShRogSlS5dCpVJRpnRpkpJTDJI35z7esGFHnvt49eqNAGze7KvtqD7cx/fv39Orr1WrOmfPXuDatRsABAQEF3pU41lZN2tChfLliuW5C6Jpi8ZcunCZhIuJZGRk4rtlN527d9KrSbyczJmYcyjFNIerdrO6XL2Ywn+Xr6LOyOTojoM0c7TRqzl1OJoH9x4AcD7iLJWqVnnuOa2trTh//iLx8ZfJyMhg00ZvnF266tU4u3Rh7ZpNAGzd4qft+N29ew+1Wg1AqZIlUZ7Drra2tuJ83KO8GzfuyJ3XuStrVmvybtHJeyIyhpRkTRsWE3OGkiVLYmJiQs1a1Tmn897bt+8gbu7dDZu5CPbx7dt3ADA2LkEJ4xIoBvwPKIo2DuCjjzz49de/AFAUhevXU585Y1GcN8qVK4utbWuWLlsLQEZGBunpNwG4det/2u2WeaNMofd385ZNiD9/mUsXE8jIyGDbZl+69bDXq3F0cmDDWs0nHz7b/LHt1AaATg7tiI0+Q8xJzSepqanpZGVlIYRACEGZN0oDUK7cG1xJ+a9QOZ+rLKXof4pBcXduVUKIRUKIaCGEvxCitBCimRDiiBDihBBiixCiUs6VhBD7hRDW2beHCCHOCCEOAO11alyFEEeFEBFCiD1CCFMhhJEQ4qwQ4q3sGiMhxDkhxJs5n+NpWJibkXA5WXs/MTEFcwuzHDVVSUjQ1KjVatJv3qRKlVwv7bkxt6hKQkKS9n5iYjIW5lVz1VzOrlGr1aSnazJrXov+uuYWVUlKSmHOnPmcjzvG5UsR3Lx5kz17Ag2TN6/nNDfNt0atVnPz5q3H7uO4uIvUr1+HGjUsUalUuLo6Ymlpnm/968y06lskJ17R3k9JvoKp2VvFmCi3iqaVuZF0TXs/Nfk6lUwr51vfwcOBqP0R2vvGJU2Ysv0XJm6ZSfMcnWJDMtNpCwCSEpMxz/Fxq5m5qV57cfPmLSpnH8vW1lYcDdnJ4WN+jBg+SdsRUxSFrdtXcCB4G4OHvGewvObmVUlIzNG+5WwrzE21NZr2Lfd7z93diROR0Tx48IDzcfHUf6cO1atbZL/3umJhwPdeUe1jIyMjgg97Excfwr6Ag4SGRhosc1G0cRUqlAdg6tQxHD7sw5o1//D2289+uiuK80bt2jW4du06SxbPIeTYLhbM/40yZUpr63744TvOx4Xw/vu9mDb9t2fODlDVzJQknWM5OekKVXMcF1XN3yYpMUWb/+bNW1SqXJHadWqCorBm40J27t/AsOEfA5CZmcmE0T+yN3gr4bH7qfdOHdau2lSonM+V7NwWiXrAX4qiNAbSgD7ASuA7RVGaAlHA1PxWFkKYAdPRdGq7ArpTFYKBNoqiNAfWAeMURckCVgMDsmu6AJGKolzTWQ8hxFAhRKgQIjRLffuJL0KI3Mty/oYp8igy5G/9T6sgefKuyX/dihUr4OrajXr121C9RgvKvFGGDz7oXcx589/HaWnpDB8+kVWr/mLv3o1cvJhAZmZm4cO+il6w4zcvT/P/38a9AzWb1mHnwkdzU8e2+4Ifen7HwuF/8P6UIbxVvfDz+/LOmXtZrmOZPIsACA2NpLVNd+w6ujN6zDDtvD/Hzv3o2L4nfXp9zGefD6Jde8N00A3x3mvYsB4/zPiO4d9oPqJOS7vJiG8ns2LVn/jvWc/Fi4moDfjeK6p9nJWVhW1bFxrWb0fLlk1p2Ki+ATMbvo0rUUKFpaU5hw+H0ratM0ePhjFr1qRiyJj/uiVUKpo3b8KCBSuxadWN27fvMG7c19qaKVN+oXYdG9au3cKXXw555uyabLmXFfS4UJVQYdOmBV8PHYe70yCcnDtj27E1JUqU4MOP+9OtU19aNLQjNvoM34z8rFA5pcIr7s7tBUVRjmffDgPqABUVRTmQvWwF0PEx67cG9iuK8p+iKA8AL53HLIFdQogoYCzQOHv5UuDD7NsfA8tyblRRlIWKolgrimJtpHrjiS8iITEZy2qPRmotLKqSnJSSu8ZSU6NSqahQvjw3bqQ9cdtFJTEhWW+U0sLCjKTkK7lqqmXXqFQqKlQoz40bqdmvRX/d5KQrdO7cgfj4S1y7doPMzEy2bvWjbRtrw+TN6zmTr+Zbo1KpKF++3BP3sa/vHjp2dMPOrhdnz57n3Ll4g+R91VxJvoqZxaPOXlUzU66mXHvMGs9fasp1Kps/GpWqZFaFtKu5P4Jt1L4JLl/3wfPTWWQ+eNShelj73+WrnDoSTfXGtXKtawhJiSnatgDA3MKM5BzTj5KSUvTai7yO5TOn47h9+w6NGr0DoJ3CdO2/63hv96eltZVB8iYmJmNpkaN9y9lWJKZoazTt26O85hZV+XfdAoZ+OpoLFy5p1/Hz3Yt9p150tu9j8PdeUe3jh9LTbxEcdJQuXR93eno6RdHGXb+eyu3bd9i2bScAmzf70KzZu8+esQjOGwmJySQkJHMsRPMpyqbNPjRv1iTXc69bt4VevXo8c3bQjNTqfqpqZm7KlRzHhaamqjZ/+fLlSE1NJznpCkcOhpJ6I417d+8RsDuId60a0bhJAwAuxl8GYMfWnbRsbbi52EVOySr6n2JQ3J1b3YmmauBZvmKY36+t84A/FUVpAnwOlAJQFOUycEUI4YCmc+z3DM+pJzQ0krp1a1GzZjWMjY3x8HDD23u3Xo23924GDeoHQJ/ezuzff7CwT1soIaHH9TL393DD29tfr8bb2/9R5j7O7MvO7O3tT38PN83cuZrVqFu3FsdCIrh8KZFWrVtQunQpABzsbTmV/cWAwsq5j/v1c81zHw8c2BeA3r17sH//oSdu9623NHMuK1aswNChg1iWPe9L0hcVEUON2tWxqG6OsXEJevTqSsAuw0w5MZQLkecwrWnGm5ZvozIuQWvX9hzfHaJXU71xLT6c+Tmen87i1vWb2uVlyr9BCRPNZb/LVipHvZYNSD6bUCQ5w8JOULtOTWrUsMTY2Jg+fV3w9dmjV+Prs5f3B/QBwL2XEwcOHAbQTqEBqFbNnHr1a3PxUgJlypSmbFnNL+JlypTGobMtsTFnDJa3Tt1Hefv2dc2d13cPAwZq8vbSyVuhQjk2bVrKtCm/cuRImN46j9575fls6EBWLPfCUIpiH1d5szIVKmjmxZcqVRI7+/acPX3eYJmLqo3z8dmjvUqFvX17YmOfvU0uivPGlSv/kZCQRP36mi92OTjYEhurOXbr1n30C6ariyOnT8c9c3aA4+EnqVWnOtWqW2BsbIxb7x74++3Tq/HfuY9+72u+kOns5sjBwKMAHNh7kIaN61Mq+zslbdpbc/Z0HCnJV6j3Th3tlJaOdu04Z8DjQno2L9ofcUgHUoUQHRRFCQIGAQceU38UmCuEqALcBPoBDydBVQASs2/nvG7RYjTTE1YpiqIubGi1Ws2IEZPx8V6DkcqIFcu9iIk9w9QpYwgLj8TbezfLlq1j+bK5xMQEk3ojjYGDHl3m5czpw5QvXw4TE2N6unbD2fkDYk+d5eeZE+nf350yZUpzPi6EZcvW8uOM2YWNq8387YhJ+Pj8i8rIiOUrvIiJOcPUqWMIC9NkXrpsHcuXexIbE0xqahoDBmoyx8ScYcPGHZyI3EemWs3wbyeSlZXFsZAINm/24dixXWRmZhJ5PJpFi9cYLO+IEZPZsWMVKpWKFSu8iI09w5QpowgLi8LHZzfLl3uxdOkfREcHcuNGGh9++OijrdOnD1KunGYfu7p2w8VlIKdOneX336fRpIlmNsvMmX9w7twFg+R9WmOnziIk4gRpaTfp7D6QLz8ZRJ8cX9QoTmq1mh/H/8oSL0+MVCo2/budc6fP8813n3PyeCz7dgXybrNG/Ln8V8pXKI+9oy1fj/sc1479n1vGLHUWq6csZtTKSRipjAheH0DS2QTcR/YnPiqO43tC8ZgwiJJlSvHl36MBuJ54jXmf/YJZXUs+mjkURVEQQuD7zxaSzhVN51atVjN29DS2bFuBSmXEqpUbOBV7lomTRhAeHoWf715WrvBi4eLZHD8RQGpqOkM+Gg5A23bWjBz1BRmZmWRlZTFqxBRuXE+lZs1qrFk3H4ASKhUb1m9nz27D/PKhVqsZPWoqW7ev1OaNjT3LpMkjCQ+PwtdnDyuWe7F4yRwio/aRmprO4A+/AeDzLz6idp0afDfhG76boFnm5voh//13nV9/m0KTJg0BmPWzp0Hfe0Wxjxu/24D5C39DpVJhZCTYssmXnTsDDJq5KNq4SZN+ZunSP/jtt6lcu3aDoUNHFyqjoc8bACNGTmblinmYmBhz/sIlPv1Uc4WCn36aQP36dVCysrh4KZGvvircJePUajWTxv3Ev5sWYqQywmvNFs6cimPMhK+JPB7Nbr99rFu1Cc/5swgO8yMtNZ0vPxkDQHr6TRb+vQLfvV4oKATsDmKvv+Y9NufXv9nss4KMzEwSLycz8sviu6zkU3tF/4iDKK55c0KImoC3oijvZt8fA5QFtgLzgTLAeWCIoiipQojl2fUbhRD7gTGKooQKIYYAE4Bk4DigUhTlayGEGzAHTQf3CGCjKIpd9nMZA9eBVoqinHpcTpOSli/V//yLNg+yIFRGqicXvWBuJewv7ghPrUmj59fRNIR2ZWoUd4SntvG/8OKO8FSyXsL2wiiviZMvsAfql28evzqr0GM+z93bb7w815Z9KDE1utgP5v+N6lnkjUDZ2duf++sstpFbRVHigXd17v+fzsNt8qgfrHPbTuf2MvKeN7sNyO9K5lZovkj22I6tJEmSJEnSq6q4Lt1Y1F60aQlFTggxHhjGoysmSJIkSZIkSa+I165zqyjKLGBWceeQJEmSJEkqVq/oyG1xXy1BkiRJkiRJkgzmtRu5lSRJkiRJkoCs4rkObVGTI7eSJEmSJEnSK0OO3EqSJEmSJL2O5JxbSZIkSZIkSXqxyZFbSZIkSZKk15EcuZUkSZIkSZKkF5scuZUkSZIkSXoNKS/hn+AuCDlyK0mSJEmSJL0y5MitJEmSJEnS60jOuZUkSZIkSZKkF5scuZUkSZIkSXodvaIjt7Jz+wRZL9lka1HcAZ5BlvJq/vm/F01UjFdxR3hqb9bsWtwRnsrL1l7cy3xQ3BGe2svWxlUsXba4Izy1m/fvFHcESSoU2bmVpGfQpFH/4o7wVF7Gjq0kSZJUtBQ5citJkiRJkiS9Ml7Rzq38QpkkSZIkSZL0ypAjt5IkSZIkSa+jV/QrL3LkVpIkSZIkSXplyJFbSZIkSZKk19Cr+oUyOXIrSZIkSZIkvTLkyK0kSZIkSdLrSI7cSpIkSZIkSdKLTY7cSpIkSZIkvY7k1RIkSZIkSZIk6cUmR24lSZIkSZJeQ/JqCZIkSZIkSZL0gpMjt5IkSZIkSa8jOedWkiRJkiRJkl5scuRWkiRJkiTpNSTn3Eq5dHO0I/pkIKdighk39qtcj5uYmPDvmn84FRPMoeAd1KhhqX3su3FfcyommOiTgTh27QRAyZIlOXzQm7DQ3UQeD2DqlNHa+i+HDeZUTDCZDxKpUqXSM+V1dLTj5MlAYmOCGZtP3jVr/iE2JpiDOfKOG/c1sTHBnDwZSNfsvABnzxwhInwPoSH+HDnsq10+efIo4i+EEhriT2iIP927Ozx75qgDxMQEM3ZMPplX/01MTDDBQTkyj/2KmJhgTkYd0Mv89defEBG+h+MRe/nmm08eZZ40igvnQwk5touQY7ueOXNB2Nq3xe/QRnYd3cxn33yU63HrNs3ZtGcVJ5MO082l6HIUxqSZs+no/B7uA78o1hydu3QkNHw3EZEBjBz1ea7HTUxMWLbCk4jIAPbu20T16hYAtGjZlKBDOwg6tIPgw964uDpq16lQoRwrV/9JSLg/x8J2YdOquUEzd+nakfDje4mM2seo0bn3n4mJCStWziMyah/7DmzRZm5pbcWhIz4cOuLD4SO+uPZ8lPnv+b9wIT6EYyE7nzmXodu0x20zvzZt9KgvtO3G8Yi93L97iUqVKhYov6HbuPr162izhIb4c/3aKYZ/8ylguDZOl0PnDhwO3cmxCH+Gj/wsj/zGLFo2h2MR/uzcu55q2cdFteoWXEqJZF/QVvYFbeW3OdMBKF26FP+uX8ChED+CjngzedroXNssDMeudkSd2E9MdBBjxnyZR14TVq/6m5joIIICt2v3d+XKFdm1y4vr107xx5wf89z2po1LCQ/bY9C8AHadbQk85k1wmB9fjfg0j8zG/LPk/wgO82PH7rVYVjPXPtawcX2271pDwKFt7Dm4hZIlTXijbBn8Azdpf6LOBTN95niD55aezkvRuRVC7BdCWD+hZrAQ4s/nlcnIyAjPuT/h4jqQJlb29O/vTsOG9fRqPh7yPqmp6TRoZMsfnov4eeZEABo2rIeHhxtNmzng7DKAeZ4zMTIy4v79+3Rx9KCldVdaWjvSzdGO1q1aAHDocAjdnN4jPv5yofK6ug6kqZU97+WTNy01nYaNbJnruYiZOnn7e7hh1cwBF528D3Xp2g9rG0fatO2ht725nouwtnHE2saRnTsDninz3LkzcO05CCsre/r3d6NhA/3MQ4a8R2paOo0a2eLpuYiZP32vydxAs4+bNXPAxXUgnp4/YWRkRONG7/DJx+/Trr0LLa0d6dGjC3Xr1tJuz3PeImxadcOmVbdnylzQ1zXll3F89v63uNh64NzbkTr1a+nVJCemMGH4dLw37yqSDIbg3qMr82fPKNYMRkZG/D57Gn17f0wr62706efKOw3q6tV8+FE/0tLSaW7lwN9/LWP6j98BEBtzBrsO7nRo50of9yH84TkDlUoFwKxfp7BndyA2LRxp38aFM6fPGTTz7Dk/0Nt9MNYtHOnXrycNcmT+aLAHaWnpWDWx5695S/hxhuZkGRN9mg7te9KujTPu7h/h6fmTNvOaVZtwdx9cqFyGbtMet8382rTfZ8/XthuTJs0iMPAIqalpBc5vyDbuzJk4bZZWrbtz585dtm7z026vsG1czvyzfp/Ce30/pX0rZ3r1caH+O3X0agZ82I+0tJu0au7I/L+XM2X6GO1j8RcuYd/BHfsO7owdOVW7/K95S2ln44RDh160at2Czl06Fiqnbt65c2fQ0+1DrJo50N/DjQY52+fB75GWlkajxh3wnLeYn2Zo2ud79+4zffr/MX583u2Hm1t3/nf7tkFy5sz8028TGdjvC+zb9MS9Tw/q5djH7w/qQ3r6TWxbOrHon5VMnDYKAJVKheeCWYwf/QMO7dzo5zKYjIxMbv/vDo4d+2h/Ei4n4eu92+DZi0zWc/gpBi9F5/ZF1MqmOXFx8Vy4cImMjAzWr99GT9duejU9XR1ZtWoDAJs2+eBgb5u9vBvr12/jwYMHxMdfJi4unlY2mpGh27fvAGBsXIISxsYoiuYjg+PHo7l4McFgeb3Wb8M1R17XfPK6unbDK5+8RcnGplmufeyqM7qWK/NmH+y1mR1z7WMbm2Y0aFCXo0cjuHv3Hmq1mqDAI7i5dS/y16KraYvGXLpwmYSLiWRkZOK7ZTedu3fSq0m8nMyZmHMv9EdG1s2aUKF8uWLN0NLaivPnLxIff5mMjAw2b/TG2bmLXk0P5y78u2YzAFu3+NHJri2A9hgAKFWqpPa9Vq5cWdq3t2HlivUAZGRkkJ5+y2CZra2tOB/3KPPGjTtwdumqV+Ps3JU1qzcBsGWLH3Z27XJnLlkSRefwOHjwGKk3ntwJzE9RtGmP22ZB2rT+/d1Y57X1mfIbuo1zcLDl/PmLXLqUWKA8T6tFy6bEn7/IxfgEMjIy2LrZByfnzno1Tj0c8Pp3CwA7tu6iQ6e2j93m3bv3OBh0FNAcxyciYzCzMDVI3lzt84btebfPqzcCsHmzD/b27QG4c+cuhw6FcO/+/VzbfeONMnz77Wf8/LOnQXLqat6yCfHnL3PpomYfb9vsS7ce9no1jk4ObFi7DQCfbf7YdmoDQCeHdsRGnyHm5GkAUlPTycrS77nVql2dN9+qzNFDYQbPLj2dIuncCiHGCSGGZ9+eI4QIyL7dWQixWgjhKIQ4LIQIF0JsEEKUzX68pRDigBAiTAixSwhhlmO7RkKIFUKIGdn3hwghzgghDgDtdepchRBHhRARQog9QgjT7HXPCiHe0tnWOSHEm8/yGs0tqnI5IUl7PyExGXPzqvnWqNVq0tNvUqVKJczN81jXQrOukZERoSH+JCeeYO/eQI6FRDxLvDzzJug8Z2JiMhYFzGthnnvdh3kVRcHPdy1Hj/jx6ScD9Lb35bAhhIftZtHC36lYscJTZ7YwNyPhcrLO86ZgbmGWo6YqCQnJjzLfzN7HFmba5QCJCSlYmJsRHXOaDh1aU7lyRUqXLkX37g5YWj762GnYF4MJC93NwgX/90yZC8K06lskJ17R3k9JvoKp2VtF8lyvOnNzUxIT9I8RM3P9k7eZeVVtjVqt5mb6LSpnfwze0tqKIyF+HDrqy8hvJ6NWq6lZsxrXrt3g7/m/EnRwO/P+nEmZMqUNmLkqCYk5juuc70VzU22N5ri+pf3o3tqmGSGhuzgaspNvv52o7ewWOlcRtGkF2WZ+SpcuRTdHOzZv8X1yMUXXxj3U38MNrxwd7cK2cbrMzE1JTEzR3k9KvIKZmf6xXNXMlESd4+LmqNYq5gAAIABJREFUzVtUrqw5LqrXsCQgaAvbfFbRpm3LXNsvX6Ecjk72BB04XKicD+X8P89zf+vs14d5nzStbtrUsfzxxyLu3r1rkJy6qpqZkqTz3ktOukLVnPvY/G2Ssv8fHmauVLkitevUBEVhzcaF7Ny/gWHDP861fbc+zmzf/OzTgoqDklX0P8WhqEZuA4EO2betgbJCCGPAFogCJgFdFEVpAYQCo7Ifnwf0VRSlJbAU+ElnmyWANcAZRVEmZXd8p6Pp1HYFGunUBgNtFEVpDqwDximKkgWsBh72wLoAkYqiXMsZXggxVAgRKoQIzcrK+6MRIUSuZYqiFKDm8etmZWVhbeNIjVrW2Fg3p3Hjd/J8/qdVVHk72bnTqnV3XFwHMmzYYGxtWwOwYMFK3mnQjpbWjiSnXOW3X6c8Q+bcywqWWcl33VOnzvHb//2Nn+9avHes5kRUDJmZmZrMC1fSoGF7rG0cSUm5yq+/TH7qzAVSgP8LqWDyO2b1a3Kv93B/h4VG0sbGCftOvRg1+gtKljShRIkSWDVrzJLFa+jQvie379xlZB7zYg2buWDHNUBoyHFsrLvRqYMbo8d8ScmSJsWc63Hvw2c/1l1cHDl0OLRAUxLyz1b4Ng7A2NgYFxdHNm7y1i4zRBv35GwFOy6upFyleWN7HDr0YvLEWcxf/Dtly72hrVGpVCxcMpvF81dxMf7ZPwF8+ry513vc/3/Tpo2oU6cG27cXTQexQOcU8ixCVUKFTZsWfD10HO5Og3By7oxtx9Z6ZW69ndi6qWC/jElFq6g6t2FASyFEOeA+cBhNJ7cDcBdNR/SgEOI48BFQA3gHeBfYnb18EmCps80FwElFUR52eFsD+xVF+U9RlAeAl06tJbBLCBEFjAUaZy9fCnyYfftjYFle4RVFWagoirWiKNZGRm/kVUJiQjLVdEb8LC3MSE6+km+NSqWiQoXy3LiRSmJiHusm6a+bnn6TA4GH6OZol+fzP63EhGS9EUoLCzOSCpg3ITH3ug/zPnzN//13na3b/LCxaQbA1avXyMrKQlEUlixZg3X28qeRkJiMZbVHI7UWFlVJTkrJXWNp9ihz+fLcuJGW/Xp11rWsSlKyZt3ly9fRuo0Tnbv0JfVGGufOXcideem/2tdiaFeSr+p9NFjVzJSrKbl+x5IKIDExBQtL/WMkJcdxnaRTo1KpKF+hXK6P78+cjuP2nbs0avQOiYnJJCamEBYaCcC2rX5YWTXGUBITk7G0yHFc53wvJqZoazTHdTlu5Mh8+nQcd27foZGBfgEuijatINvMT3+PngWekvAwW1G0cQDdu9sTERHF1auP3qeGaON0JSWmYKEzWmxuYUpKylW9muSkFCx0jovy5cuRmprGgwcZ2l8CThyPJv7CJerofJdg9twfOR8Xz4J/VhQqo66c/+d57u/EFO1+fZg353Gsq03rljRv3pTTpw8RsHcz9erVwt9/vcEyJydd0fv0z8zclCu59vEV7aj9o32cTnLSFY4cDCX1Rhr37t4jYHcQ71o9GlNr9O47lCihIioyxmB5nws557bgFEXJAOKBIcAhIAiwB+oAF4DdiqI0y/5ppCjKJ4AAonWWN1EURXcCzyHAXghRSvep8okwD/hTUZQmwOdAqexcl4ErQggHNJ1jv3zWf6KQ0OPUrVuLmjWrYWxsjIeHGzu8/fVqdnj7M2hQPwD69HFm3/6D2uUeHm6YmJhQs2Y16tatxbGQCN58szIVKpQHoFSpUnR26MDp03HPGvGxeft7uOGdI693Pnm9vf3pn0feMmVKU7aspvNfpkxpunbpRHS0Zj5S1apva7fr7uakXf40QkMjc+1j7xwT9b29dz/K3NuZ/drMu3Pt45CQ4wC89VYVAKpVM8fd3Qkvr225Mru5dX+mzAURFRFDjdrVsahujrFxCXr06krArsAiea5XXXjYCerUqUmNGpYYGxvTu68Lvr579Wp8fffywYDeALj3ciIw+2PZGjUstV/GqlbNnHr1anHxUgJXr14jMTGZuvU0nYNOdu04fcpwXygLCztBnbqPMvft64qvj/63wn199zBgYB8AevVy4kCemS2oV782lwoxF19XUbRpBdlmXsqXL0fHDm3Yvr3gX6gsijbuof793XNNSTBEG6crIjyKWnVqUj37uHDv7cxOX/0vqe30DaD/B70AcHXvRnDgEQCqVKmk/ZJvjZqW1K5Tk4vZX9SbMGkE5SuUZeL4mYXKl5Omfa756P+2X8+82+eBfQHordM+52fholXUqm3NO++0w6Fzb86evYCjo4fBMh8PP0mtOtWpVt0CY2Nj3Hr3wN9vn16N/8599HvfDQBnN0cOBmrmLB/Ye5CGjetTqnQpVCoVbdpbc1bn/OzWp8dLOWr7qk5LKMrr3AYCY9CMkEYBs9GM6B4B/hJC1FUU5ZwQogyakdbTwFtCiLaKohzOnqZQX1GU6OztLQE6AhuEEL2Ao8BcIUQV4CbQD4jMrq0APJz1n/M6S4vRTE9YpSjKM09WU6vVfDtiEr4+/6IyMmL5Ci9iYs4wbeoYQsMi8fbezdJl61ix3JNTMcGkpqbxwUDNpVJiYs6wceMOoiL3kalWM/zbiWRlZWFmZsrSJX+gUmm+Zbxx4w58fDUnva+/+pgxo7+katW3iAjbg9/OAD7/YuxT5/XJkXfq1DGE6eRdvtyT2Oy8A3Tybti4gxM58pqavsXGDUsAUJVQsW7dVvz99wMw6+dJWFk1QlEU4i8m8OWX3z3TPh4xYjI+3mswUhmxYrkXMbFnmDplDGHhmszLlq1j+bK5xMQEk3ojjYGDsjPHavZxZGQA6kw13347STv532vdQqpUqURGRibDv51IWlo6AD/PnIiVVWMUReHixct8+VXRXM5FrVbz4/hfWeLliZFKxaZ/t3Pu9Hm++e5zTh6PZd+uQN5t1og/l/9K+QrlsXe05etxn+PasX+R5HlWY6fOIiTiBGlpN+nsPpAvPxlEnxxf4ClqarWaMaOns3nrclQqI1av2sip2LN8P2kEEeFR+PnuZdWK9Sxc/DsRkQGkpqbx8eBvAWjT1pqRoz8nIyMTJSuL0SOncuN6KgDjRk9n8ZI5GJsYE3/hMl8NG2fQzKNHTWXr9pWoVEasWrmB2NizTJo8kvDwKHx99rBiuReLl8whMmofqanpDP7wGwDatrNh9OgvyMjMJCsri5EjJnM9O/Oy5XPp0LENVapU4vTZQ/w04w/tl+IKmsvQbRqQ5zbh8W2au5sTu/cEcudOweddFkUbB5q5v106d8zVhhmijcuZf8KYH1i/eTFGKhVrV2/i9KlzfPf9cI5HnGSXXwBrVm3k74W/cSzCn9TUdIZ+PBKAtu1t+O774WRmqsnKUjNm5FTSUtMxMzdl1NhhnDkdR0Cg5otoSxatZvXKjYXK+jDviBGT8d6xGpVKxfIVXsTGnmHKlNGEh53A22c3y5avY9nSP4iJDuLGjTQGffjo8mynTx+ifLlymJgY4+raDWeXAZw6dbbQuZ6UedK4n/h300KMVEZ4rdnCmVNxjJnwNZHHo9ntt491qzbhOX8WwWF+pKWm8+UnmitSpKffZOHfK/Dd64WCQsDuIPb6PxqUcHXvxiCPYUWaXyo4UVRz/YQQnYGdQEVFUW4LIc4A8xVFmZ09cvoLUDK7fJKiKNuFEM0ATzSd0xLAH4qiLBJC7AfGKIoSKoSYDtRHM3f2I2ACkAwcB1SKonwthHAD5qDp4B4BbBRFscvOZQxcB1opinLqSa+jhInFSzUZMo/ZQi+8vOZuvehqVzB7ctELJCrG68lFL6A3a3Z9ctELRJ1VTMMUz+he5oPijvDUXrbWomLpssUd4andvH+nuCM8tTdLly/uCE8tMTW62A/na906FXkf581dB5776yyykVtFUfYCxjr36+vcDgBs8ljnOJrR2ZzL7XRuT9V5aBl5zJtVFGUbsC2faFZovkj2xI6tJEmSJEmS9HJ5rf78rhBiPDCMR1dMkCRJkiRJei0V15zYovZa/REHRVFmKYpSQ1GU4OLOIkmSJEmSJBneazVyK0mSJEmSJGnIkVtJkiRJkiRJMiAhRHchxOnsvxqb52WKhBAeQogYIUS0EOLfJ21TjtxKkiRJkiS9hop75FYIoQL+QvOXZhOAECHEdkVRYnRq6qG5MlZ7RVFShRBv5721R+TIrSRJkiRJklQcWgHnFEU5n/3XZtcBbjlqPgP+UhQlFUBRlKs8gezcSpIkSZIkvY4UUeQ/QoihQohQnZ+hOgksgMs69xOyl+mqD9QXQhwUQhwRQnR/0suS0xIkSZIkSZKkIqEoykJgYT4P5/UHHnL+YYkSQD3ADs1ftA0SQryrKEpafs8pO7eSJEmSJEmvoeKec4tmpLaazn1LICmPmiOKomQAF4QQp9F0dkPy26icliBJkiRJkiQVhxCgnhCilhDCBHgP2J6jZitgDyCEeBPNNIXzj9uoHLmVJEmSJEl6DSlZec0KeI7PryiZQoivgV2ACliqKEq0EOIHIFRRlO3ZjzkKIWIANTBWUZTrj9uu7NxKkiRJkiRJxUJRFF/AN8eyKTq3FWBU9k+ByM6tJEmSJEnSa+gFmHNbJOScW0mSJEmSJOmVIUdun0Bl9HL1/9VZL+GvYUrOq368+NqVqVHcEV4L1+J3F3eEp1atrnNxRyiw0sYmxR3hlXcn435xR3hqIs+rM73Y1K/qEGQRU5SX7/+6IGTnVpJeA2/W7FrcEZ7ay9ixlSRJkoqf7NxKkiRJkiS9hl7VAW/ZuZUkSZIkSXoNFfelwIrKyzWhVJIkSZIkSZIeQ47cSpIkSZIkvYZewu9zF4gcuZUkSZIkSZJeGXLkVpIkSZIk6TUk59xKkiRJkiRJ0gtOjtxKkiRJkiS9huTIrSRJkiRJkiS94OTIrSRJkiRJ0mtIXi1BkiRJkiRJkl5wcuRWkiRJkiTpNSTn3EqSJEmSJEnSC06O3EqSJEmSJL2GFEWO3EqP4djVjqgT+4mJDmLMmC9zPW5iYsLqVX8TEx1EUOB2atSwBKBy5Yrs2uXF9Wun+GPOj9r60qVLsXXLck5E7iMifA8zfhxv8MzdHO2IPhnIqZhgxo39Ks/M/675h1MxwRwK3qHNDPDduK85FRNM9MlAHLt2AsDS0pw9/huIOrGfyOMBfPP1J4XO6Ohox8mTgcTGBDM2n4xr1vxDbEwwB3NkHDfua2Jjgjl5MpCu2RkBzp45QkT4HkJD/Dly2Fe7vGnTRgQFbicifA9btiynXLmyhc6v691OzZi5dy4/759Hj2HuuV/rJy7M2D2H6X6/M2bNVKpYvKl9bHGcF9N8f2Oa7298s+g7g+bKqXOXjoSG7yYiMoCRoz7P9biJiQnLVngSERnA3n2bqF7dAoAWLZsSdGgHQYd2EHzYGxdXR+06FSqUY+XqPwkJ9+dY2C5sWjUv0teQn0kzZ9PR+T3cB35RLM//kH1nW4JDfDkcvpOvR3ya63ETE2MWLJ3N4fCd+O5ZR7Xq5trHGjauj7f/Wg4c3sG+g9soWdIEgPGTviXsZABxCaEGz+vQuQOHQ3dyLMKf4SM/yzPvomVzOBbhz86966mWfUxUq27BpZRI9gVtZV/QVn6bM127jtemxewL3kbQEW9+mzMdIyPDno4Mnbl06VL8u34Bh0L8CDrizeRpow2aF6Br105EHN/Liaj9jB49LI/MJqxY+Scnovaz/8BWqlfXtHcODrYEH9zBsWM7CT64g06d2uZad/2GRYSE7DJ43hMn9hEdHZjveW/Vqr+Ijg4kMHBbjvPeOq5di2XOnB/01unb15WQkF2Eh+/hp5++N2heePnee9KzeW6dWyFEvBDizTyWHyrq5yhqRkZGzJ07g55uH2LVzIH+Hm40aFBPr2bI4PdIS0ujUeMOeM5bzE8zNG/ae/fuM336/zF+/Ixc253zxwKaWtnTqrUTbdvZ0M3RzqCZPef+hIvrQJpY2dO/vzsNG+pn/njI+6SmptOgkS1/eC7i55kTAWjYsB4eHm40beaAs8sA5nnOxMjIiMzMTMaOm06Tpna0t3Vl2LDBubb5LBldXQfS1Mqe9/LJmJaaTsNGtsz1XMRMnYz9PdywauaAi07Gh7p07Ye1jSNt2vbQLlsw/ze+nziT5i26sG2rX54nl2cljIwY+MOnzBn8E5O6jqR1T1vM61rq1VyKucAPrt8x1Wk0oX6H6TdhkPaxB/ceMK3HWKb1GMu8z34xWK6cjIyM+H32NPr2/phW1t3o08+VdxrU1av58KN+pKWl09zKgb//Wsb0HzWd7diYM9h1cKdDO1f6uA/hD88ZqFQqAGb9OoU9uwOxaeFI+zYunDl9rshew+O49+jK/Nm532vPk5GRET//32Q+6DuUjq1d6dXXmfrv1NGr+WBQX9LS0mnbojsL/l7JpGljAFCpVPy18FfGjZpGp7au9Hb5iIyMTAD8d+7HqXP/Isk76/cpvNf3U9q3cqZXH5dceQd82I+0tJu0au7I/L+XM2X6GO1j8RcuYd/BHfsO7owdOVW7/JPB32Jv60aHNi68+WYlevbq/sJn/mveUtrZOOHQoRetWregc5eOBs08e84P9HIfTMsWXenXrycNcrz3PhrsQVpaOk2b2PHnvCX8OEMz6HH9eip9+35Cq1bdGfrZaBYvmaO3Xk+3btz+3x2DZX2Yd+7cGbi5fUSzZp3x8OiZ67w3eHB/0tLSady4I/PmLWbGjAnAw/Pe74wf/5NefeXKFfn55+9xcnqfFi26YGr6Jvb27Q2a+WV67z0PSlbR/xSH59K5FUKo8ntMUZR2zyNDUbKxaUZcXDwXLlwiIyOD9Ru246ozagXg6urIqtUbAdi82Uf7hr1z5y6HDoVw7/59vfq7d+9x4MBhADIyMjgeEYWFpZnBMreyaa6fef02erp206vp6erIqlUbANi0yQcHe9vs5d1Yv34bDx48ID7+MnFx8bSyaU5KylUijp8E4H//u82pU2exMK9qsIxe67fhmiOjaz4ZXV274ZVHxsepX78OQUFHANizN4hevXo8tv5p1G5Wl6sXU/jv8lXUGZkc3XGQZo42ejWnDkfz4N4DAM5HnKVS1SoGe/6CamltxfnzF4mPv0xGRgabN3rj7NxFr6aHcxf+XbMZgK1b/Ohkpxklunv3Hmq1GoBSpUqiZF9jply5srRvb8PKFesBzfGcnn7reb0kPdbNmlChfLliee6HmrdsyoXzl7h0MYGMjAy2bvKlWw8HvZpuPRxYv3YbAN7bdmHbqQ0Adg7tiTl5mpiTpwFITU0jK0tz9ggPjeTqlf8MnrdFy6bEn7/IxfjsvJt9cHLurFfj1MMBr3+3ALBj6y465DFymNP/bt0GoESJEhgbGxv0mkRFkfnu3XscDDoKaI7hE5ExmFmYGiyztXUzzsc9eu9t3LgDFxf984iLsyNrVm8CYMsWX+zsNKfPyMhoUpKvAhATc4aSJUtiYqIZVXzjjTJ8882n/PLLPINlhdznvQ0bduR53lutPe/55jrv3b9/T6++Vq3qnD17gWvXbgAQEBCMu7uTwTK/bO896dk9sXMrhBgnhBiefXuOECIg+3ZnIcRqIcT7QogoIcRJIcQvOuv9TwjxgxDiKNBWZ3lpIcROIcRnD+uy/7UTQuwXQmwUQpwSQqwRQojsx3pkLwsWQngKIbyzl1cRQvgLISKEEAsAofM8W4UQYUKIaCHE0Oxlnwgh5ujUfCaEmF2YHQhgbl6VywlJ2vuJicm5OnXm5lVJyK5Rq9XcvHmLKlUqFWj7FSqUx9m5C/v2HSxs1Ed5LPQzJyQmY54zs06NWq0mPf0mVapUyvV6ExKTMbfQX7dGDUuaWb3L0WMRhcqY8KT9mk9GC/Pc6z7MqCgKfr5rOXrEj08/GaCtiY4+rW2c+/ZxoZqlOYZS0bQyN5Kuae+nJl+nkmnlfOs7eDgQtf/RvjMuacKU7b8wcctMmufoFBuSubkpiQnJ2vuJiSmYmeufwM3Mq2pr1Go1N9NvUTn7WG5pbcWRED8OHfVl5LeTUavV1KxZjWvXbvD3/F8JOrideX/OpEyZ0kX2Gl50ZmZvk5SYor2fnHQFM7Mc+9jMlKTER/v41s1bVK5ckdp1a6IAazctwv/AJr4aXvipP0/Ma25Kok7epMTceauamZKok/fmzVtUrqw5JqrXsCQgaAvbfFbRpm1LvfXWb15MbNwh/ve/22zfariPzIsyM0D5CuVwdLInKHsAwhDMzU1JSNRvs3K+93Rr8juPuLs7cSIymgcPNL8oT5kyGk/Pxdy5o9+RLHzePNrYXHmf7rwXF3eR+vXrUKOGJSqVCldXRywN2A6/bO+95yFLEUX+UxwKMnIbCHTIvm0NlBVCGAO2wFngF8ABaAbYCCEeTiZ8AzipKEprRVGCs5eVBXYA/yqKsiiP52oOjAAaAbWB9kKIUsACwElRFFvgLZ36qUCwoijNge1AdZ3HPlYUpWV25uFCiCrAOqBndn6AIcCyAuyDx8rug+tRcoxC5FGSqyYvKpWKVSv/5K+/lnHhwqVnzphTwTLnVfPkdd94owzrvRYxasxUbt363wuXsZOdO61ad8fFdSDDhg3G1rY1AJ8NHcWwLwZz9IgfZcu9wYMHGc+cPaeCvJaH2rh3oGbTOuxcuE27bGy7L/ih53csHP4H708ZwlvVDTdi9OScOWtyr/fwtYSFRtLGxgn7Tr0YNfoLSpY0oUSJElg1a8ySxWvo0L4nt+/cZeTo4p3zWpzy3McU7LguoVLRuk0LvvpsLG7dB+Dk0gXbjm2KLGv+WQqSV+FKylWaN7bHoUMvJk+cxfzFv1O23BvaGo/en/JufVtKljShQyfDvY6izKxSqVi4ZDaL56/iYnzCc82c15tPt6Zhw3r8OGM833yjmfbWtGkjatepwY7thp1rq4ny7Ps4P2lp6QwfPpFVq/5i796NXLyYQGZmZuHDPi7PC/zek55dQTq3YUBLIUQ54D5wGE2HsQOQBuxXFOU/RVEygTXAw0lIamBTjm1tA5YpirIyn+c6pihKgqIoWcBxoCbQADivKMqF7Jq1OvUdgdUAiqL4AKk6jw0XQkQCR4BqQD1FUW4DAYCLEKIBYKwoSlTOEEKIoUKIUCFEqFr95M5ZYmKy3iifhYUZSclXctSkaH8DValUlC9fjhs30p647b///oVz5y4w788lT6x9GokJ+pktLcxIzplZp0alUlGhQnlu3EjN9XotLcxITtKsW6JECTZ4LWLt2i1s3epX6IyWT9qv+WRMSMy97sOMD1/nf/9dZ+s2P2xsmgFw+nQcPZw/oHUbJ7y8tnH+fHyh8utKTblOZfNH08ErmVUh7WpqrrpG7Zvg8nUfPD+dReaDR436w9r/Ll/l1JFoqjeuZbBsuhITU/Smv1hYVCUlxz5P0qlRqVSUr1CO1BzH8pnTcdy+c5dGjd4hMTGZxMQUwkIjAdi21Q8rq8ZFkv9lkJR0Re+TDjNzU+1Hyo9qUjC3eLSPy5UvR2pqGklJVzh8MIQbN9K4e/cee3cH0tSqUdHmTUzBQievuYUpKSn6eZOTUrDQyVs+O++DBxmkpmqOjRPHo4m/cIk6dfWP3fv3H7DTNwCnHvrTBl7UzLPn/sj5uHgW/LPCYHkh+xxhod9m5ToudGpynkfMLaqydt0CPvt0lHYgpFXrFjRv3oSY2GD27N1A3Xq18Nu5zkB582hjc+TVrSnoec/Xdw8dO7phZ9eLs2fPc+5cvEHywsv33nseFEUU+U9xeGLnVlGUDCAezSjnISAIsAfqAI8bSrynKIo6x7KDgJPI61cjDd2Jp2o0lyp70p7J9WugEMIO6AK0VRTFCogASmU/vBgYzGNGbRVFWagoirWiKNYq1ZO/MR8aGkndujWpWbMaxsbGePTribf3br0ab+/dDBrYF4DevZ3Zv//JUwymTRtLhfLlGD1m2hNrn1ZI6HHq1q31KLOHGzu8/fVqdnj7M2hQPwD69HFmX3bmHd7+eHi4YWJiQs2a1ahbtxbHQjQfoS9a+Duxp87xx9yFBs/Y38MN7xwZvfPJ6O3tT/88MpYpU5qyZTWjMGXKlKZrl05ER2vmUL31lmaOqxCC7yd8y8KFqwr9Gh66EHkO05pmvGn5NirjErR2bc/x3SF6NdUb1+LDmZ/j+eksbl2/qV1epvwblDDRXLWvbKVy1GvZgOSzhhsx0hUedoI6dWpSo4YlxsbG9O7rgq/vXr0aX9+9fDCgNwDuvZwIzP5o9uFHiQDVqplTr14tLl5K4OrVayQmJlO3nqaD0MmuHadPFc8Xyl4Ex8OjqF2nBtVrWGBsbIx7nx74++3Tq/H324fH+24AuLh142CgZi74/r3BNGz8DqVLl0KlUtG2vQ1nTscVad6I8Chq1alJ9exjwr23Mzt9A/RqdvoG0P+DXgC4uncjODtvlSqVtF/krFHTktp1anIx/jJvvFEGU1PNh3AqlYoujp04e+b8C50ZYMKkEZSvUJaJ42caLOtDYWGR1Kn76L3Xt68rPj765xEf390MGNgHgF69enDggOb72BUqlGfzpmVMnfIrR46EaesXL1pN3TqtadTQli6d+3Hu7AWcur9nkLya896j9rlfP9c8z3sDtee9Huzf/+Tvjz9shytWrMDQoYNYtmztE9YouJftvSc9u4Je5zYQGAN8DEQBs9GM6B4B/si+QkEq8D7wuFnrU4DJwN9AQb+KfgqoLYSoqShKPKD7lcRAYAAwQwjhBDyczFMBSFUU5U72CK32swNFUY4KIaoBLYCmBczwWGq1mhEjJuO9YzUqlYrlK7yIjT3DlCmjCQ87gbfPbpYtX8eypX8QEx3EjRtpDPrw0WWtTp8+RPly5TAxMcbVtRvOLgO4desWE8YP59Spsxw9ohkB/Wf+cpYtM8xv3Wq1mm9HTMLX519URkYsX+FFTMwZpk0dQ2hYJN7eu1kq9ObJAAAgAElEQVS6bB0rlntyKiaY1NQ0PhioudRLTMwZNm7cQVTkPjLVaoZ/O5GsrCzat7Nh0MC+nIiKITRE0wmdPHkWfjsDHhfliRl9cmScOnUMYToZly/3JDY74wCdjBs27uBEjoympm+xcYNmFFxVQsW6dVvx998PwHv93fli2GAAtm71ZfkKr0LsYX1Z6ixWT1nMqJWTMFIZEbw+gKSzCbiP7E98VBzH94TiMWEQJcuU4su/NZcYup54jXmf/YJZXUs+mjkURVEQQuD7zxaSzhVN51atVjNm9HQ2b12OSmXE6lUbORV7lu8njSAiPAo/372sWrGehYt/JyIygNTUND4e/C0AbdpaM3L052RkZKJkZTF65FRuXNeMOI8bPZ3FS+ZgbGJM/IXLfDVsXJHkf5KxU2cREnGCtLSbdHYfyJefDKJPji8pFjW1Ws33Y2ewdtNiVCoj1q7ezOlT5xj3/TccjziJv98+/l21kT8X/MLh8J2kpabz+ceaYyI9/SYL/lrOzoANKIrC3t2B7PE/AMDk6WPo1deZ0mVKEx6t2cb/zfrLIHknjPmB9ZsXY6RSsXb1Jk6fOsd33w/neMRJdvkFsGbVRv5e+BvHIvxJTU1n6McjAWjb3obvvh9OZqaarCw1Y0ZOJS01nbfeqsKqdf9gYmKCSmVEcOARli81TNtWVJnNzE0ZNXYYZ07HERCo+SLakkWrWb1yo8Eyjx41hW3bV6JSqVi5cj2xsWeZNHkk4eFR+PrsYcXy9SxeMpsTUftJTU3jow+/AeDzLz6kdp0ajJ8wnPEThgPQ03UQ//133SDZ8ss7YsRkduxYhUqlYoX2vDeKsLAofHx2/z979x0VxdXGcfx7WbB3RJot1thR0aixYS/YjUnUWBJjoq+xaxJbjN3ElhgTNfYee0VExIbBhmLvgtIFBYnGAsu8fywurGAEBRF5Pufskdl7Z/jtOMzeeXZmlmXL/mLJkjlcuHCIe/ci6dFjgHH+K1eOkDvB+56zc3cuX77GzJnjqVTJUBGdMmUO16/7vijCK2XOSH97b8K7+g1lKjnnfSqlGgOuQD5N0x4qpa4C8zVNm6WU6gp8j6HC6qJp2si4eR5ompYrwTL8MJzOcBdYAoRpmjbyWb+4autwTdOc4/r/BpzUNG2ZUqoN8DMQDhwHrDVN6xZ3Hu1aoCBwEOgIVAf+AbYC9sAVDOfpjtc07UDcsr8DHDRNe+khbNZsRVLvEt43QB+bTvfdeA0Z8U/rM7uXXw3+NtkU/uoX9qWXcL+9L+/0FipSqnV6R0g2fXrdpycT+Tf6ycs7vWUy4vtIvmw5X97pLRMSeSnd3/4ulW6V5mOcctdc3vjrTFblVtO0fYBFgukyCX5eA6xJYp5cz00XTzDZ+/l+cQPPAwmeH5Cg/35N096PO51hHnAyrs9dIOG9R4Yk+Pm/7h9SF5j9H+1CCCGEECIDyihfv/ulUqonkAXD+bMLXmUhSql8GCq/Z+IG7EIIIYQQmdK7elpChhjcapo2m1SotGqaFgmUeWlHIYQQQgiRIWWIwa0QQgghhEhd6fUlC2ntjXz9rhBCCCGEEG+CVG6FEEIIITKh9PqShbQmlVshhBBCCPHOkMqtEEIIIUQmlIyvOsiQpHIrhBBCCCHeGVK5FUIIIYTIhORuCUIIIYQQQrzlpHIrhBBCCJEJyd0ShBBCCCGEeMtJ5VYIIYQQIhOSuyUIIYQQQgjxlpPKrRBCCCFEJvSu3i1BBrcvochY//H5suVM7wgpFpsBPxfZGHYqvSOkSEZcxxmV//Vd6R0hRapX7JbeEVIko+2TXStlTe8IKVbexz+9I6RYj3wO6R1BvEVkcCuEeCsVKdU6vSOkWEYb2AohMje5W4IQQgghhBBvOancCiGEEEJkQu/qObdSuRVCCCGEEO8MqdwKIYQQQmRC7+qlxjK4FUIIIYTIhOS0BCGEEEIIId5yUrkVQgghhMiE5FZgQgghhBBCvOWkciuEEEIIkQnFpneANCKVWyGEEEII8c6Qyq0QQgghRCakIefcCiGEEEII8VaTyq0QQgghRCYU+45+i4NUboUQQgghxDtDBreppGnTBpw9u58LFw4xfHj/RO1ZsmRh5cp5XLhwiEOHtlGsWGEAChTIx5496wgPv8Ts2RNM5uncuQ0nTuzh1Cl3Jk8eleqZGzWpx1FvV4777GXgkL5JZLZg0dI5HPfZyx6PDRQpam/Sbl/YFr+g0/zvm8+Nz/Xt14PDR3fieWwXX/Xvmap5Gzepx7FTezjp486goUnlzcLiZXM46ePOXo+NSea9HezDgIFfmDxvZmbGAc9trN2wMFXzAjRpWh/v0+74nPVgyLCvk8y8dPmv+Jz1wOPAZorGZa5evTKeXjvx9NrJkaO7cG7TzDjPuYuH8Dq+G0+vnRw4vC1NMp/y2ceZc/sZ+oLMy1fM5cy5/ew/uCU+s2MV/j66i7+P7sLrqAtt2sZn/n3+dHz9TnD8hGuq53VqXBfPEy54nXJlwOA+SeS1YMGSWXidcsXFfR1FitoZ28pVKMNOt7Uc9NrB/iPbyJo1CwDfjRmE93kPbgScTPW8KTFmyizqt/6E9t0T/z+klw+darHdcx07vTbw+YDPErVXr+XAX27LOBVwmKbOTsbny1YozcqdC9l8cDUbPVbSvF3jN5a5jtMHbPNcyw6v9UlmrlbLgXVuS/EOOEST5zKv2LmQzQdXscFjxRvNnPWDGlitWY7VulXk7P5povbsLZtTaMcWCi79k4JL/yS7cytjW+5+fSm4YgkFVywhWyOnRPOmlcZN6nP8lBveZ/YxeOhXidqzZMnC4uW/4H1mH3v3J95HFy5si3/ImUT76DehTIMqjNg3k5EHZtOwX9tE7bW6NWGI63QGu0yl34YfKFTKPomlZDyxqDR/pIe3YnCrlOqllLJLMO2nlCqYBr/HRSmVL+6ReAT6iszMzPjll0m0a9cTB4fGdOnSlvffL23Sp1evj4mMvE+FCvWZO3cRkyZ9D8Djx0/48ceZfPfdZJP+BQrkY+rUUbRs+SnVqjXB2rogTk4fplZkzMzMmD7zBz7u9CUf1mhFx87OlClb0qRPtx4fERl5n5oOTZk/bxk//DjCpH3S1FHs23vIOP1+udJ81rMLzZw606BOW5o1d6JEyWKplvenmePp0rEPtWu0pFNnZ8qWLWXSp3uPzkRGRuHo0IQ/5i1l/ATTvFOmjTbJ+8zX/Xty9cqNVMn5fOaZs36kU4fe1KjenM4ftaHs+6aZe/TsQmRkFA6VGzHvtyX8OPFbAC5evEqDuu2oW9uZju178cvcSeh0OuN8rVt2pW5tZxrWa5fqmWfNnkDH9r1wrNaMjz5qy/vPZe7ZqwuRkfepUsmJeXMXM3HSd4bMF65Q78O21KnVmvbte/Lrr5ONmVev3ET79r1SNeuzvFNnjKVr577U/6ANHTq3TrQdd/2sM5GR96ldrQULfl/BmPHDAdDpdMxb+BMjh46nQe02dHTuSXR0DABurgdo2fjjVM+bUu1bNWX+rEnpHcPIzMyMUVOH0a/rUNrX/5SWHZpSokxxkz7BgSGMGTSR3Vv2mjz/+NFjRn8zgY4NutHv0yGMnDCY3HlyvaHMw+nfdRgd6nelRYcmiTKHBIYwdtCkJDOP+WYCHRt0p/+nQxkxYdAbyYyZGXmGDuLe8O8I696L7E0aY1488b70scd+wnt/SXjvL3m00wWArLVrYVGmNOG9+3C3b39ydv0YlSPHG4hsxs+zxvNRxy+o5diCTh85J9rffdbzI+5H3qd6lcaGffTEkSbtk6ePxj2JfXRaU2aKDhN6s7jXdGY2HY5D2zqJBq+ntx1hdotvmdPqew4u2EmbsYkPksTb460Y3AK9ALuXdUoOpdQLzyPWNK2VpmmRQD4g1Qa3NWo4cOOGH76+t4mOjmbDhh20SVBpA2jTphmrVm0EYPNmF+NA9d9/H/H33yd48uSxSf/33ivKtWu+hIffA8DDw5P27VumVmSqOVbG9+Ytbvn5Ex0dzZZNu2jZuolJn5atG7Nu7RYAtm91pV7D2gnamnDLz58rl68bnytTtiTeJ87w6NFj9Ho9fx85TmvnpqmSt/pzeTdv2kVLZ9MqSqvWTVi3ZjMA27a6Uj9B3lbOTfDz8+fypWsm89jZ2dC0eUNWLl+fKjkTcnSsws2bt/CLy7xp485E66O1cxPWrt4EwNYtu2nYsA6AcR0CZMuaFe0NnRfl6FiFmzfiM2/cuCNx5tZNWb3KkHlLMjMfOXKciHuRqZ63avXK+N68ze1bAURHR7N1kwvNWzUy6dO8VSPWrzVUuHdu20PdBrUAaNjoQy6ev8LF81cAiIiIJDbWcNfHUyfPcCc0LNXzppSjQyXy5smd3jGMKlYtz23fAAJvBxETHYPrVnecmtc36RPkH8K1SzeM6/KZWzf9ue0bAEBYaDj3wiPIb5nvjWT2fy5zw+b13urMFuXeRx8QhD4oGGJieOTuQda6yStumBcvxlOfM6CPRXv8mJjrN8haq2YaJzZ8cnMz4T564y5aJXpPacLa1Yb3lG1bXGnw3D76lm/iffSbUMShFOG3Qrjnfwd9tJ4zO7yo0MzRpM+TB4+MP2fJkRXtTe2U05iGSvNHenilwa1SaqRSamDcz7OVUh5xPzdWSq1SSjVTSnkppU4ppTYopXLFtY9TSp1QSp1XSi1UBp0BR2C1UspHKZU97td8Ezf/OaXU+3Hz51RKLYlbxmmlVLu453vF/Z4dgJtSylYpdShueeeVUvXi+j2rCE8DSsa1//zqq8/Azs6GgIAg43RgYDB2dtYv7KPX64mK+gdLy/wvXOaNG7coU6YkxYoVRqfT0aZNMwoXTpXxPwC2ttYEBYQYp4OCQrB9LrOtrTWBAcEmmQsUyE+OHNkZOORLfp72m0n/SxevUftDR/IXyEf27Nlo0qwBdoVtUymvDYGBwfF5A0OwtX0ur501gXGvSa/XE3X/AQUsDXkHDenLT1PnJlrulOmjGT/2p0RvaqmS2c6GgICEmYOxSyJzwPPrOG67cHSswrETrngd383ggWOMA0dN09i6fTkHPbfRq/cnqZrZzs6GgATrOTAwBDs7m+f6WBv76PV67ifYlh1rOHDi5B6OnXBl0KDRxsxpxda2EEGB8dtxcFBo4u3C1pqgBHn/ifqHAgXyUaJUcTRg7aY/cTu4if+lw0ehGY21rRWhQXeM06HBdyhka5Xi5VSsWh4LCwv8/QJTM16SCtlaERIUapy+ExyG9StlLvfGMuusCqK/E7+eY8PC0Fkl/jAzW4P6FFy2iHwTx2NWyPCaoq/fIOsHH0DWrKi8echSzQFdoZS/3pQy7H+f20cneh987j3FZB/9FdOT2Ee/CXmt83M/6K5x+n7wXfJYJ35/rv1ZU749OIdW33Vl+/jlbzKiSKFXvVvCIWAY8CuGgWlWpZQFUBc4B4wBmmia9lAp9S0wFJgA/KZp2gQApdRKwFnTtI1KqQHAcE3TTsa1AYRrmlYt7vSB4UAfYDTgoWna50qpfMBxpZR7XKbaQGVN0+4ppYYBezRNm6yU0gHPfybzHVBR0zSHV3z9JuLymnj+qC45fRKKjLzPwIGjWblyHrGxsRw96s177xV9/bApyJNkHzS+HTWQ+fOW8fDhvyZt167e4NfZf7Jp61IePvyXC+cuo4+JSaW8iZ9L7jr+bvRA/vhtaaK8zVo4ERZ2lzM+F/iwbupXNpKVOamj2rg+J0+e4YMaLShTtiQLFs5gr9sBnjx5SrPGHxEScoeCVpZs27GCq1dv8PeRE6mU+fW25ZMnfKjh2JyyZUuy4M+ZuO0xZE4rL9pGX9pHA3Odjg9qVaOF00c8evSYDduWcsbnAp6HjqZZ3gwvhfuxpBQsZMmUueMYM3DiG6l+Jefv8GUKFrJk8txxjBk46c1U7JIObTL5+IgXj9w9IDqaHO3akG/0d9wbNIynJ07ypFxZCs7/jdjISKLPX0TTp/33UCXrPe6F++hB/DEv8T76jUlyfSd+ymvlXrxW7sWhbR0afdOB9cP+SPtsaexd/YayVx3cegPVlVK5gSfAKQyD3HrAdqA8cCRuY88CeMXN56SUGolhsFkAuADseMHv2Jzgd3WM+7kZ0FYpNTxuOhvwbMS3V9O0e3E/nwCWxA24t2qa5pOSF6eU6gv0BTA3z49O99/nWAUGBptUVe3tbQkOvpNkn8DAEHQ6HXny5ObeSz6mdXFxx8XFMHb/4ouu6FNxBxUUFIJd4fiKnJ2dDSHPZQ4KCsG+sC3BQaHGzBH3IqnmWIU27Zrzw4QR5M2bh1gtlsdPnrJ44SpWr9zI6pWG0y9GjxtKUFAIqSEoKAR7+/gqsJ29DSEhz+UNDMG+sA1BQXHrOG8uIu5FUt2xCm3btWD8xJGGvLGxPH78BFs7a1q2akzTZg3Imi0ruXPnYv6fM/j6y+HP//pXyxwYQuHCCTPbEvx85iBDH2PmJLaLq1du8PDhv5QvX5bTp88ZX3d42F12bnczXMiVSoPbwMBgCidYz/b2NgQHhz7XJ4TC9rYExW3LeZPIfOXKDf59+C/lK5Tl9KlzqZItKUFBodjZx2/HtnbWSW7Hdvbx23HuPLmJiIgkKCgUryMnjNn37T1E5SrlZXD7H0KD7mBtV8g4bW1biLCQ8GTPnzNXDuatmsnc6Qs5e+pCWkRMJDQoDJsEFcRCtlbcSWHm31bN4LfpCzn3hjLr74ShKxS/ns2srNCH3zXpo0VFGX/+d8cucveLv8j2wYrVPFixGoB8P4xB7x+Qxomf7X+f20c//7cY1+f5fbRjjSq0a9+CHxPso588ecqfC1ameW6A+yH3yGtnaZzOa2tJ1J2IF/Y/s8OLDpPkk5632SudlqBpWjTgB/QG/gYOA05AScAXw0DTIe5RXtO0L5RS2YDfgc6aplUC/sQwOH2RJ3H/6okfhCugU4JlF9U07VJc28ME+Q4B9YFAYKVSqkcKX99CTdMcNU1zfNnAFgwVtlKl3qN48SJYWFjw0Udt2LnT9MKEnTv30r17ZwA6dmzFgQN/v3S5VlaGP7Z8+fLSt+9nLF26NiUv4z+d9j5HiRLFKVqsMBYWFnTo1BpXl30mfVxdPPjk0w4AtG3fgsMHDccobVp0pVqlRlSr1IgFfyxnzoz5LF64CoCCBQsAhjsTOLdtxuaNO1Ml7ynvc5QoGZ+3Y6fWuO4yzbvbZR+fdDUcB7Vr34LDBw2DlNbNu+JQ0QmHik7M/30Zs2fOZ9HCVUwcP5OK79fDoaITfXoN5vCho6k2sAXw9j5LiZLFKRaXuVNnZ1x2uZv0cdm1j0+7dQKgfYeWHIxbx89ORwEoUsSO0mVKcOt2ADlyZCdXrpwA5MiRnUaN63Lp4tVUzVyyVHzmzp3bJM7s4k637obMHV6Y2Z7SZUpw+1bavqn6nDpHiZLFKFrMHgsLC9p3aoXb7v0mfdx276fLp4YL75zbNedI3OD1wD5PylUoS/bs2dDpdNT+sEaaXFj4Lrngc4liJYpgX9QWcwtzWrRvwgG3w8ma19zCnDlLp7Njw2727vBI46TxLvhcomiJwiaZD7p5JmtecwtzZi+dFpd5/8tnSCXRly+jK2KPztYGzM3J3qQRT46YvmeYWRYw/py1bh1ibt2OazBD5cljyF+yBOYlS/DkROoc/P6XU95nKVmyWPw+unNrdid6T9nHp90M7yntOrTgUNw+ulWzT6lSoSFVKjTkj9+XMWvGH29sYAsQcOYGBYvbkL+wFToLHVXa1ObiXm+TPgWLxx9Ev9+oKnf9Uqdwk97e1XNuX+dLHA5hOF3gcwynIszCUGU9CsxTSpXSNO26UioHUBh4dggXHncObmdgY9xz/wDJuWpiD4Zzcb/RNE1TSlXVNO30852UUsWAQE3T/lRK5QSqASsSdEnu70sWvV7P4MFj2bFjJTqdjuXL/+LSpauMGzcUb+9z7Nq1l2XL/mLJkjlcuHCIe/ci6dFjgHH+K1eOkDt3brJksaBNm+Y4O3fn8uVrzJw5nkqVygMwZcocrl/3Ta3I6PV6vhsxgQ1bFmOm07Fm5UauXL7Od6MH4nPqPK67PVi9YgO/L/yZ4z57iYy4z5e9h7x0uUtX/UaBAvmIjo5h5LAfuR8Z9dJ5kpt35PAf2bh1CTozHatXbuTy5et8P3oQp0+fw9XFg1UrNjD/zxmc9HEnIiKSPsnIm5b0ej0jho1ny7bl6HRmrFyxgcuXrjF6zGBOnTrHbpd9rFj+FwsXzcLnrAcREffp3XMgALXrODJk6NdEx8QQGxvL0MHjuHc3guLFi7B63XzA8LH6hvXbU/XqYr1ez7ChP7B1+wpj5kuXrjFm7BBOnTqHyy53li/7i0WLZ3Pm3H4iIu7Tq8c3cZlrMGxYfOYhg8dy966h+rF02S/Uq18LS8v8XLn2N5MnzWFFKlzEp9frGTViEms3LUKnM2Ptqs1cuXydkaO+wef0edx272fNyo38tmA6XqdciYy4z1efDwPg/v0oFsxbhqvHBjRNY9/eQ7i7HQRg7I/D6dC5NdlzZOfUBcMyZkyb99p5U2rED9M4cfoskZFRNG7fnf5ffEanNs3feI5n9Ho9U0bN5I+1c9DpzNi6dic3rvjSf+SXXPS5xAE3Tyo4lGPOkmnkyZebBk3r0m9EHzo26Ebzto2pVsuBvPnz0PZjw22rxg6axJULaXsBkV6vZ+qoWfyxdjZmOl2CzH244HOZg3GZZy+Zaszcf8QXdGzQPcnM4wZNTvPM6GOJmvUrBWb9BGZmPNq1mxhfP3J90Zvoy1d4cuRvcnbuaLjITK8nNiqKyMnTDPOa67Cc9wsA2r//EjlhMryB0xL0ej0jh/3Ipq1L0el0rF5p2N99P2YQPqfOs9tlHyuXr2f+opl4n9lHREQkX/QanOa5kiNWH8u2ccvos+J7zHRmnFh/gNBrATQb0pmAc75cdPemTs9mlPqwErExMTy6/5C/3oFTEt5l6lXPH1JKNQZcgXxx59ZeBeZrmjZLKdUImA5kjes+RtO07UqpScAnGKq+/sAtTdPGK6U6AVOARxjOnb0EOGqaFq6UcgRmaJrWMO5iszlAHQxVXD9N05yVUr3i+g+Iy9YTGAFEAw+AHpqm+Sql/BIsdw1QGditaZrpPaMSyJataIa6JDJXlv8qhr+dYjPgVacxsWl7oVRqy4jrOCNuy/7Xd6V3hBSrXrFbekdIkSTPU3+LuZbK+vJOb5nyPv7pHSHFvixYI70jpNhPfmvTfWN2tf4kzd8cWoSue+Ov85Urt5qm7QMsEkyXSfCzB5BoS9M0bQyGi82ef34TsCnBU8UTtJ0EGsb9/AhIdGdoTdOWAcsSTC8HEl3KqGlawuV2Tep1CSGEEEKIjOt1TksQQgghhBAZlNwtQQghhBBCvDPS64KvtPa2fEOZEEIIIYQQr00qt0IIIYQQmVDsu1m4lcqtEEIIIYR4d0jlVgghhBAiE4qVc26FEEIIIYR4u0nlVgghhBAiE8p4X++TPFK5FUIIIYQQ7wyp3AohhBBCZELv6pc4SOVWCCGEEEK8M6RyK4QQQgiRCcUquVuCEEIIIYQQbzWp3AohhBBCZELv6t0SZHD7EmOt66V3hBSZHXEivSOkWOMC5dM7QortDjub3hFS5HHM0/SOkGLZLbKkd4RMwfv86vSOkHL66PROkCI1q3ye3hFSpGLeYukdIcW+rRSU3hHEW0QGt0IIkUqqV+yW3hFSJEMObDOYjDawFZmL3C1BCCGEEEKIt5xUboUQQgghMqHYd/NmCVK5FUIIIYQQ7w6p3AohhBBCZEKxvJulW6ncCiGEEEKIdKGUaqGUuqKUuq6U+u4/+nVWSmlKKceXLVMGt0IIIYQQmZD2Bh7/RSmlA+YBLYHywKdKqUT3B1VK5QYGAseS87pkcCuEEEIIkQnFqrR/vERN4LqmaTc1TXsKrAPaJdFvIvAT8Dg5r0sGt0IIIYQQIk0opfoqpU4mePRN0GwP+CeYDoh7LuH8VYEimqbtTO7vlAvKhBBCCCEyoTfxJQ6api0EFr6gOanarvFsBqWUGTAb6JWS3ymVWyGEEEIIkR4CgCIJpgsDCb9LOTdQETiglPIDagHbX3ZRmVRuhRBCCCEyoZdd8PUGnABKK6XeAwKBT4Cuzxo1TbsPFHw2rZQ6AAzXNO3kfy1UKrdCCCGEEOKN0zQtBhgA7AEuAes1TbuglJqglGr7qsuVyq0QQgghRCb0Nnz9rqZpLoDLc8+Ne0HfhslZplRuhRBCCCHEO0Mqt0IIIYQQmdCbuFtCepDKbRoo0aAyX3v8TL+DM6ndr02i9mrdGvPlnmn0cZlCj43jKFjacEs3uyol6OMyxfDYPYWyzV/6DXOvpVGTehz1duW4z14GDumbqD1LFgsWLZ3DcZ+97PHYQJGiJreew76wLX5Bp/nfN58bn/v6f73wPLaLw0d3snDJLLJmzZIm2R0aVOUXj9+Ze3A+7ft1StTu3Kcts91/Y4brL4xbM4GC9lYAFC//HpO3TGfW3rnMcP2FOs510yTfM02a1ueUzz7OnNvP0GFfJ2rPkiULy1fM5cy5/ew/uIWicevYqVFdDh/ZzrHjuzl8ZDsNGtQ2ztOpU2uOHtvNiZN7mDjphd9U+J+aN2vIhfOHuHzRk5Ej/pdkrjWr/+DyRU/+9txBsWKFjW3fjhzA5YueXDh/iGZNG7x0mf379eLyRU9ingZiaZnf+PywoV9z8oQbJ0+44XN6H08e3SZ//nwpfi2NGtfD66Qrx0+7MXDIl0m8Fgv+XDqb46fdcN233rgdFylqz+2QM+w/vJX9h7fy8+wfjfP8tWkR+z23cfjoTn6e/SNmZmm3q/zQqRbbPdex02sDnw/4LFF79VoO/OW2jFMBh2nq7GR8vmyF0qzcuZDNB1ez0WMlzds1TrOMKTFmyizqt/6E9t0Tb+/pxfOYN7Q6scoAACAASURBVM7d+tHy074sWrUxUXtQyB2+GDyGDr2+odfAUYTcCTe2zfpjGe17DqB9zwHs3nf4jWWu4/QBWzzXss3rL3oP6J6ovVqtKqxxW8KJgIM0cW5ofN62sDWr9yxmnfsyNh5cRece7d9I3poNa7D60DLWeq6g2/8+SdRe5YNKLHadz/5bbjRsXd+krZBdIWaumc7KA0tYuX8JNoWt30hmi2o1yffHSvItWE22zl2T7JOlrhN55y0n77xl5Bo+FgAzK2vyzl5I3l8WkXfeMrK2eOVTQ0Uaeesrt0qpfEBXTdN+T+8syaHMFC0m9mJNt6lEhdzj8+0TueZ+ivBrgcY+57f9zanV+wAo3aQaTcZ0Y13Pn7hzJYDFbcag6WPJVSgffXZP4ar7KTR96h9bmZmZMX3mD3Ru15ugwBD2HtiEq8s+rl65YezTrcdHREbep6ZDUzp0as0PP46gT+/BxvZJU0exb+8h47SNrTVffvUZH9ZsxePHT1i0bA4dOrVm3ZotqZ79i4lfMbHbD9wLucvU7TM46X6cgGvx94H2veDLt85Defr4Kc26t+Cz73sxe8DPPHn0hLlD5hDiF0z+QgWYvmsmPodO82/Uw1TN+CznrNkTaOv8GYGBIRw6vA2XXe5cvnzd2Kdnry5ERt6nSiUnOnd2ZuKk7+jZ4xvu3r3HR537EBJ8h/Lly7B1+3LKlKpNgQL5mDTle+p92Jbw8HssWDiDhg3rcODA3ynK9esvk2nR6lMCAoI56uXCjp1uXLp0zdjn896fEhFxn/fL16VLl7ZMnTKart36Ua5cabp0aUdlh0bY2VmzZ/c6ylWoB/DCZf7tdYJdLu7s22s6qJg5az4zZ80HwLl1UwYN/JKIiMgUr+NpM8fxUfveBAWG4rZ/I64uHklsx1HUrNqM9p1aMe7H4XzZewgAfr63caqX+M3/i16DePCPYZtYuvJX2nZowdZNLon6vS4zMzNGTR1G3y6DCA2+w1rXJRxwO8zNq37GPsGBIYwZNJFe/buZzPv40WNGfzOB274BWFkXZJ3bUv7ef4x/oh6kes6UaN+qKV07tWXUxBnpmuMZvV7PpNkL+HPWBGysLPm47zCc6takZPGixj4zfl9C2+ZOtGvZmGPeZ5izcAXTxgzloNcJLl67wcbFv/A0OppeA0dRr1Z1cuXMkaaZzczM+G7qMPp1GUxo8B1Wuy7ioJvnc9tFKD8MmkyP/p+azBsWepdebb4m+mk02XNkZ+PBlRzc40lYaDhpxczMjKGTBzLk05GEBYfxp8vvHHHzwu/aLWOf0MA7TBnyE598/VGi+cf88i0rfl3DycPeZM+RjdjYN3ANv5kZOb8eTNTYYcTeDSPvrAVEHzuC3j8+s5mtPdk7dyNq5P/QHj5A5TUcfMdG3OX+iP9BTDRky06+35by9PgRtHt30z53KpPKbfrJB/RP7xDJZedQknt+oUT6hxEbrefijqOUaVrdpM/TB4+MP1vkyGr8OebxU+NAVpfVAi0N/76rOVbG9+Ytbvn5Ex0dzZZNu2jZuolJn5atG7NurWFgun2rK/Ua1k7Q1oRbfv5cSTBQAzA3Nydb9mzodDpy5MhOSMidVM9eyqE0IX4h3PEPJSY6hiM7DuPYtKZJnwte53j6+CkAV09foYCtJQDBvkGE+AUDEHHnHvfD75OnQJ5Uzwjg6FiFmzdu4Re3jjdu3EFr56YmfVq3bsrqVZsA2LJlNw0b1gHg7JmLhAQb1t3Fi1fJmjUrWbJkofh7Rbl+zZfw8HsA7N9/hHbtW6QoV80aVblxww9f39tER0ezfv022rZpbtKnbZtmrFy5AYBNm3bRyKlu3PPNWb9+G0+fPsXPz58bN/yoWaPqfy7Tx+cCt24F/Gemjz9ux7q/tqbodQBUq14Zv5u3uOUXQHR0NFs376Jla9MKZstWjfgr7gBrx9Y91EtQBX+RZwNbc3NzLCwsSKs/xopVy3PbN4DA20HERMfgutUdp+amVa0g/xCuXbpBbKzp29Ctm/7c9jWs17DQcO6FR5DfMuWV79Tm6FCJvHlyp3cMo3OXrlHU3pYidjZYWFjQsnE9PDxNv57+hp8/H1SvAkDNapXZH9d+w8+fGlUqYm6uI0f2bJQtWRzPY6fSPHPFquXwT7Bd7Nm6j4bN65n0CTZuF6bbZkx0DNFPowHIktUCpdL+iqFyVd8n0C+Q4NvBxETHsG/bfuo2r2PSJyQglBuXbqI9l7d46WLozHWcPOwNwKN/H/Pk8ZM0z2xeuhz64EBiQ4MhJoYnhzyw+MD0k7xszdvw2GUL2kPDAaN2P+7gOybGMLAFlIUFpOEnO+LVZIT/kWlASaWUj1LqZ6XUCKXUCaXUWaXUjwBKqeJKqctKqUVKqfNKqdVKqSZKqSNKqWtKqZpx/cYrpVYqpTzink/8GeZrym1TgH+C44/eooLvkdsmf6J+1Xs0pf+hWTT+/lP2/LDc+LydQ0n67p1O3z3TcB29JE2qtgC2ttYEBYQYp4OCQrC1s07UJzDAMBDU6/VERf1DgQL5yZEjOwOHfMnP034z6R8SHMq8uYvxuXCAC9eOEBX1Dwc8jqR69gI2ltwNjq9C3Au+i6WN5Qv7N/64KacPeCd6vlSV0phnMSf0VkgSc70+OzsbAgKDjdOBgSHY2dk818fa2Eev13M/6h+Tj+4B2rdvydkzF3j69Ck3b/hRpmxJiha1R6fT0aZNU+wL26Usl70N/gHx98gOCAxOnCtBH71ez/37UVha5sfOLol57W2StcwXyZ49G82bNWTzlpRXRm3trAkMTLAdB4Zia2u6HdvYWhMYmHg7BiharDAeh7ewbddKatU2PQhdv3kRl278zYMHD9m+dU+KsyWHta0VoUHxB4ChwXcoZGuV4uVUrFoeCwsL/P0CX945k7kTfhebQsbbZGJtVZA7YaYVtrKl3mPvQcOnH+6HvHj47yMi70dRtuR7HD7mzaPHT4iIjOLE6XOE3AlL88yFktgurFKwXVjbFeIvj+Xs9t7Csnmr07RqC2BlU5A7QfHrJSw4jII2Bf9jjnhFShTmQdRDJv05nsV75tN/TN80PQ3oGTPLgsSGx6/j2Lth6CxNM+vsC6OzK0Ke6b+R5+ffsagWX0QxK2hF3l+XkH/pBh5tXJMhq7YAmkr7R3rICIPb74AbmqY5AHuB0kBNwAGorpR6VuYoBfwCVAbex3AT4LrAcGBUguVVBloDtYFxSqlEI4OE34N84sH155tTTEui6uO9Yi+/1x+Kx7R11P0m/mPRIJ8bLGz6LUvajqVO/7boslq89u9PSlJH88/nTLIPGt+OGsj8ect4+PBfk7a8+fLQslVjqldqRMUydcmRIwcfffxmzkVKah0D1OvQgBKVSrF9gempEfkK5eeb2UP4ffivL5z3db3yOk7Qp1y50kyY9C0DvxkNQGRkFIMHjWX5yt9wc1/PrVuB6GNi3lCuF8+bnGW+iLNzM/72OpniUxJenDN56zg05A5VKzjRqF4Hxo6exvxFM8mVO6exT5eOfahYpi5Zs2ahXoNaKc6WLK+x3p4pWMiSKXPHMW7wpDTbljOypNbJ89vE8P69Oelzns5fDOKkzwWsrSzR6XR8WLMq9Wo50r3/SEZM+JkqFd5Hp9Olfeikqq0p+L8NDbrDx4160q72x7Tp0pICBRMXWFJVkl+gmry8OnMdlWtWZN7EBfRt1R/bora07NL85TO+rhfs40zD6dDZFSZq1CAezJhAzm9GoHLmAiA2PIz7Az8nom9XsjVugcqXxutYpEhGGNwm1CzucRo4hWEQWzquzVfTtHOapsUCF4B9mmGvdg4onmAZ2zRNe6RpWjiwH8NA2YSmaQs1TXPUNM2xRq5SKQr4T8g9ctvGVxHz2BbgQeiL37QvbPeiTLPEF47dvR7E00dPKFSmcBJzvb6goBDsCsdX1uzsbIwfgyfsY1/YFgCdTkeePLmJuBdJNccq/DBhBKfOefBVv54MHv41X/TtToOGdbh1K4C7dyOIiYlh5w43anxQNdWz3wu5i6Vt/BF2AVtL7oXeS9Sv0odV6DjgI6b3mUzM0/gBYPZc2fl+6VjWzljFtdNXUz3fM4GBwRS2tzVO29vbEBwc+lyfEGMfnU5H3jy5uXfPsL3Y2duwZt0C+vYZhq/vbeM8u1324dSgA42dOnHt2k2uX/dLWa6AYIokqPYWtrdNnCtBH51OR968ebh3L4LAwCTmDQpN1jJf5OMubV/plASAoMAQ7O0TbMf21olOhQkOCsHe/rntOCKSp0+jjQPqsz4X8PO9TclS75nM++TJU1xdPGjZKm0u1goNuoO1XSHjtLVtIcJCkl9ly5krB/NWzWTu9IWcPXUhLSJmeNZWBU0uEAsNC8eqYAGTPoUKWvLL5FFsXPwLg740XLyVO5fhQOerHl3YtOQXFs2aiIZGsRR+UvIq7rzmdvFMWGg4N674Uq1WldSMl/j3BIdTyC6+smxla0V4aPIqmXeCw7h2/jrBt4PR62Px3HOEMpVKv3zG1xQbHoZZwfh1bGZpRey98ER9nh7zBL2e2NAQYgP9MbMzfU/W7t0l5rYfFuUrp3nmtBD7Bh7pIaMNbhUwVdM0h7hHKU3TFse1JTxJJzbBdCymF849f2yWqqWOoDM3KfCeDXmLWGFmoaN8m1pc3Wv6kXj+4vEfm5Zu5ECEn+Fj1bxFrFA6w39JHvuCWJawJTIgbT4CO+19jhIlilO0WGEsLCzo0Kk1ri77TPq4unjwyacdAGjbvgWHD3oB0KZFV6pVakS1So1Y8Mdy5syYz+KFqwgICMKxhgPZs2cDoH6D2ly9cjPVs18/cw3b92wpVKQQ5hbmfNimHif3HjfpU7zCe/Sd2o/pX0wm6u594/PmFuaMWPg9Bzft56hL8i/CehXe3mcpWao4xeLWcefObXDZ5W7Sx8XFnW7dDXd76NChJQfj1nHevLnZtGkJ48f9xNGjptuPlZXh4Clfvjx82bc7y5f9laJcJ076UKrUexQvXgQLCwu6dGnHjp1uJn127HTjs88MF3506tSa/QeOGJ/v0qWd4fzf4kUoVeo9jp84naxlJiVPntzUr1eL7dtf7WP/06fO8V7J+O24fcfWuLp4mPRxdfHg466G7bhN++Z4HjoKgKVlfuPHn8WKF6ZEyeLc8vMnZ84cWFsb3qh1Oh1NmjXg2tXU344BLvhcoliJItgXtcXcwpwW7ZtwwC15V+SbW5gzZ+l0dmzYzd4dHi+fIZOq+H5pbgcEERAUQnR0NLv3Hcbpww9M+kRERhnPaf5z9UY6tDJcf6DX64m8HwXAlRu+XL3hR50aqX/A/rwLPpcpWqIwdnHbRfP2jTng5pmseQvZWpE1m+EuNbnz5sahRiX8rt9+yVyv57LPZQq/Z49tERvMLcxp3M4JT7fk7V8v+1whd77c5CuQF4BqH1bF7+qtl8z1+mKuXUZnVxgzaxswNydr/UZEHzc9je7pUU/MKxn+v1WevJjZFSE2JAgzSyvIYljHKmcuLMpVRB/on+h3iPTz1t8tAfgHeHZ1wh5golJqtaZpD5RS9kB0CpfXTik1FcgJNMRw2kOq0fSx7Bm3jE9XfIuZzowz6w8Sfi2Q+kM7EXzWl2vup3Ds2Yz36lYkNlrPo6iHbB9quGK8iGNZ6vRvQ2y0Hk2LxXXMUh5FpM2Vz3q9nu9GTGDDlsWY6XSsWbmRK5ev893ogficOo/rbg9Wr9jA7wt/5rjPXiIj7huvMH+RUyfPsmPbHjwObyUmJoZzZy+xYum6VM8eq49l8biFjF4xHjOdGfvX7yPgmj8fD+3KjbPXOel+nM9G9SZbjuwM+30kAOFB4UzvM5nazh9SrmYFcufLjVPnRgDMG/4rfhd9Uz2nXq9n2NAf2Lp9BTqdGStXbODSpWuMGTuEU6fO4bLLneXL/mLR4tmcObefiIj79OrxDQBffd2TEiWL8e333/Dt94bn2rXpQVjYXX76eRyVKpUDYNrUX7l+PWXZ9Xo9gwaPwWXXGnRmZixb/hcXL15l/A/DOel9hp0797Jk6TqWL/uVyxc9iYiIpGt3wzWdFy9eZePGHZw7s58YvZ6Bg0YbBwVJLRNgwP8+Z/iw/tjYWHHa253drh589fUIANq3a8le90P8+++jpMMm47V8P3wC6zcvwkynY+2qTVy5fJ1vRw3E5/R59uz2YPXKjYbt+LQbERH36fu5YTuu/WENvh01kJgYPbGxeoYP+YHIiPtYWVmyct0fZMmSBZ3ODM9DR1m2JPW342f5p4yayR9r56DTmbF17U5uXPGl/8gvuehziQNunlRwKMecJdPIky83DZrWpd+IPnRs0I3mbRtTrZYDefPnoe3HrQAYO2gSVy5ce8lvTVsjfpjGidNniYyMonH77vT/4jM6tXkDHzO/gLm5jlGDv+Kr4ePRx8bSoVUTSr1XlN8Wr6ZC2VI41f2AEz7nmLNgBUopqlepwJghhtuYxcTo6THgewBy5czOtDFDMTdP+9MS9Ho900fN5ve1szDT6di2dic3r/jSb2QfLvpc5qCbJ+Ud3mfWkqnkyZeb+k0/5OsRfejcoDvvlS7O0PEDnp1HxIo/1nL9ctocnMXnjWX2mLnMXDMdMzMzdv21G7+rt/hieC8un7nCkb1evF+lLJMX/0juvLmo07Q2nw/rSY9GXxAbG8u8CQuY89cMUHD13DV2rNmVpnkBiNXzcP4c8vw4A8zMeOLugv62H9m7fU7MtctEH/+b6FPHsahag7zzlkNsLP8u/QPtnyjMHRzJ/Xl/DLUxxaMtf6G/lbbrOK28q3dLUBnhHC2l1BoM58ruBgKAPnFND4DugB7YqWlaxbj+y+KmNyqlij9rU0qNB+yAkkBR4CdN0/78r989uVi3t38FJTA74kR6R0gxp/zl0jtCiu0OO5veEVLkcczT9I6QYvmz50rvCClmm73Ayzu9RbzPr07vCK9Gn9KaRvqpWeXzl3d6y+TSZUvvCCm2tVrKrj14G1juOJjuX377W5HuaT7GGeC/6o2/zoxQuUXTtOfvrvxLEt0qJujfK8HPfgnbgKuapiX+xgIhhBBCiEwkQ1XvUiCjnXMrhBBCCCHEC2WIym1q0TRtfHpnEEIIIYR4G8Sm+4kRaSNTDW6FEEIIIYTBu3pBmZyWIIQQQggh3hlSuRVCCCGEyISkciuEEEIIIcRbTiq3QgghhBCZkNwKTAghhBBCiLecVG6FEEIIITKhd/VWYFK5FUIIIYQQ7wyp3AohhBBCZEJytwQhhBBCCCHeclK5FUIIIYTIhORuCUIIIYQQQrzlpHL7Ejoy1qWEsVrGOw4zz2DrGMBMZazMGSttxqVkTb8ZOov0TpBsx8+vpE7lXukdI0Wym2Wc9fuMMotJ7wgZUuw7WruVwa0QQmRW+uj0TpByGWhgK4RIHzK4FUIIIYTIhORuCUIIIYQQQrzlpHIrhBBCCJEJvZtn3ErlVgghhBBCvEOkciuEEEIIkQnJObdCCCGEEEK85aRyK4QQQgiRCcW+o7fmlsGtEEIIIUQm9K5+iYOcliCEEEIIId4ZUrkVQgghhMiE3s26rVRuhRBCCCHEO0Qqt0IIIYQQmZDcCkwIIYQQQoi3nFRuhRBCCCEyIblbghBCCCGEEG85GdymgfcaVOZLj5/56uBMavVrk6jdoVsjPt8zld4uk+m2cSyWpe1M2vPYWTL04iJq9m2VpjkbN6nHsVN7OOnjzqChfRO1Z8mShcXL5nDSx529HhspUtTepN2+sC23g30YMPCL+Ox5c7Ns5VyOerty9KQrNWo6pEn2Kg2qMtNjHrMP/kHbfh0Ttbfq05af3ecy3XUOo9dMoKC9FQAF7a2YvHMmU11m8/PeX2nSrXma5HumSdP6eJ92x+esB0OGfZ2oPUuWLCxd/is+Zz3wOLCZonHruHr1ynh67cTTaydHju7CuU0zALJmzcL+g1s4cnQXx064Mmr04NfO2KxZQ86fP8Sli56MGPG/JDOuXv0Hly56csRzB8WKFTa2jRw5gEsXPTl//hBNmzYAoEyZkpw84WZ83A2/zMBv+gAwduxQ/HxPGttatGj02vkbNa6H10lXjp92Y+CQL5PIb8GfS2dz/LQbrvvWG7fjIkXtuR1yhv2Ht7L/8FZ+nv0jANmzZ2PN+gX8fWI3h4/uZOz4Ya+d8b/UcfqAbZ5r2eG1ns8HfJaovVotB9a5LcU74BBNnJ2Mz5etUJoVOxey+eAqNnisoHm7xmma8xnPY944d+tHy0/7smjVxkTtQSF3+GLwGDr0+oZeA0cRcifc2Dbrj2W07zmA9j0HsHvf4TeSNznGTJlF/daf0L574r/R9FK7YU02Hl7F5iNr6DmgW6L2qh9UYeWeRXjd9qBR6waJ2nPmysEu702MmPz6+4jkcGxYncUHFrH08BI+7t8lUXulDyoyz+U3dvvuol6ruiZtfUZ9wUL3BSzyWEj/H/u9kbwAFlVrkvf3leSdv5psnbom2SfLh07k/W05eeYuI+fQsQCYWVmTZ+ZC8sxeRJ65y8jaou0by5zatDfwSA9v/WkJSqlRmqZNSe8cyaXMFM0m9mRdt2n8E3KPXtsncM3dm7vXgox9Lm7zwme1BwClmlSj8ZjurO/5k7G98bhu3DxwJk1zmpmZ8dPM8XRs14ugwBD2HdyE6y4Prly5buzTvUdnIiOjcHRoQsdOrRk/YQRf9IrfUU6ZNpp9ew+ZLHfqT2PY536IXp99g4WFBdlzZEv17MrMjN4Tv2JKtx+4G3KXydt/xtv9OIHXAox9/C7cZLTzMJ4+fkqT7i3o+n1Pfh0wg4g7EfzQ8VtinsaQNUc2fnb7Fe+9x4m4E5HqOc3MzJg560fatelBYGAIBw5vxWWXO1cux6/jHj27EBkZhUPlRnTq7MyPE7+ld8+BXLx4lQZ126HX67G2seLvo7vY7bKPJ0+e4tyqGw8f/ou5uTlu7uvZ63aAEyd8Xjnjr79MpmWrTwkICOaolws7d7px6dI1Y5/Pe39KZMR9ypWvS5cubZkyZTTduvWjXLnSfNylHVUcGmFnZ43r7nWUr1CPq1dv4FijmXH5t/y82bptt3F5v/z6J7NnL3jFtZo4/7SZ4/iofW+CAkNx278RVxcPrl65YezTrcdHREZGUbNqM9p3asW4H4fzZe8hAPj53sapXvtEy503dwlHDh/DwsKCzduX0bhJffa5H0rULzXyj5o6nK+6DCI0+A5rXBdzwO0wN6/6GfuEBIYwdtAkevY3ffN9/OgxY76ZwG3fAKysC7LWbQl/7z/GP1EPUj3nM3q9nkmzF/DnrAnYWFnycd9hONWtScniRY19Zvy+hLbNnWjXsjHHvM8wZ+EKpo0ZykGvE1y8doONi3/haXQ0vQaOol6t6uTKmSPN8iZX+1ZN6dqpLaMmzkjvKIBhuxg5ZQgDPhlKaHAYy10WcmiPJ77Xbhn7hASG8uPgKXT/+pMkl/H1yD6cOvpq+4VXyTtg0v/4rusowoPDmbvzV7z2HuX2tdvGPncCw5gxdCadv+pkMm/56uWo4Fier5sZBrWzNs+kcq3KnD16Nq1Dk+OrwfzzwzBi74aRZ8YCnh4/Qqx//Do2s7UnW+duRH37P7SHD1B58wEQG3GXqG//BzHRkC07eX9dytPjR9Du3U3bzCLZMkLldlR6B0gJW4eSRPiFct8/jNhoPRd3HKV00+omfZ4+eGT82SJHVrQExzalm1Un8nYY4VcD0zRndcfK+N68xS0/f6Kjo9m8aRctnU0rP61aN2Hdms0AbNvqSv2GtePbnJvg5+fP5QSDoNy5c1GnTg1WLt8AQHR0NFH3/0n17KUcShPiF8wd/1D00TF47fDEsekHJn0uep3n6eOnAFw/fYUCtpYA6KNjiHkaA4BFFguUWdp996CjYxVu3ryFX9w63rRxJ62dm5r0ae3chLWrNwGwdctuGjasA8CjR4/R6/UAZMuaFS3B4e/Dh/8a8luYY25hjqa9+rFxzRpVuXHDD1/f20RHR/PX+m20aWNazW7TphkrVxr+Tzdt2kUjp7pxzzfnr/XbePr0KX5+/ty44UfNGlVN5m3UqC43b97i9u202Z6rVa+M381b3PILIDo6mq2bd9Gytel23LJVI/5aswWAHVv3UK9B7aQWZfTo0WOOHD4GGLbhs2cuYmtvnSb5K1Ytj79vAIG3g4iJjsF1qzsNm9cz6RPkH8K1SzeIjTW9rvnWTX9u+xoO6MJCw7kXHkF+y3xpkvOZc5euUdTeliJ2NlhYWNCycT08PI+Z9Lnh588H1asAULNaZfbHtd/w86dGlYqYm+vIkT0bZUsWx/PYqTTNm1yODpXImyd3escwqlC1HP5+gQTeDiYmOoa92/bRoLlptTM4IITrl26ixSb++3+/UhkKWOXn2METbyRvWYeyBPkFE3I7hJjoGA5uP0idZqZ/Z6EBofhe9k20v9I0yJI1C+ZZzLHIYoG5hY6I8NQvNjzPvHQ5YkMCiQ0NhpgYnh72IEtN03WctVkbnrhsQXtoOGDU7kcaGmJiDANbQFlYgFlGGEolLfYNPNLDW/U/opTaqpTyVkpdUEr1VUpNA7IrpXyUUqvj+nRXSh2Pe26BUkoX9/wDpdT0uPndlVI1lVIHlFI3lVJt4/r0UkptU0q5KqWuKKV+SO3XkNsmP/8E3zNO/xN8j9w2+RP1q9ajCV8dmonT95/g/sMKACyyZ6VWP2c852xO7ViJ2NraEBgYbJwOCgzB1tb0DdzWzprAgBDAULGJuv+AApb5yZEjO4OG9OWnqXNN+hcrXoTw8Hv8Nn86Bzy38ctvk8mRI3uqZ89vU4C7wfEfdd4Nvkt+mwIv7N/w4yacORD/JlrAtiDTXefw29FFbJ+/OU2qtgC2djYEBCRcx8HYJbGOn/XR6/VERf1DAUvD9uLoWIVjJ1zxOr6bwQPHGAe7ZmZmeHrt5IbfsIc3bwAAIABJREFUCfZ7HOHkyVev8tvZ2xAQEP+pQmBgMPZ2Non6+Mf10ev13L8fhaVlfuztEs9rZ28678dd2vHXX1tNnuvfrzenvPfy58KZ5MuX95WzQ9w2GhhinA4KDE20HdvYWhu3deM6LmBYx0WLFcbj8Ba27VpJrdqmB6FgOM2mWUsnDh/0eq2cL1LI1oqQoFDj9J3gMKxtrVK8nIpVy2FhYYG/X9oeFN8Jv4tNoYLGaWurgtwJM61WlS31HnsP/g2A+yEvHv77iMj7UZQt+R6Hj3nz6PETIiKjOHH6HCF3wtI0b0ZlZVOQ0KA7xunQ4DCskrldKKUY/MP/+HXiH2kVL5GCNpaEBcX/X4YFh2NpY5mseS+duoSP1xnWnVzDOu81nDzojf91/7SKaqQsC6IPj1/HsXfDMLMsaNJHZ1cYM7si5J72G3l++h2LqjWNbWYFrcjzyxLyLd7A481rpGr7lnmrBrfA55qmVQccgYHAz8AjTdMcNE3rppQqB3wMfKhpmgOgB56djJQTOBA3/z/AJKAp0AGYkOB31IybxwH4SCnl+HyIuIH1SaXUyeMPrj3f/BJJVAKTKKydWuHOgvrDODBtHXW+MXwsWndoR04sciX63ycp/J0pp5KK+dwRtUqik6ZpfDd6IH/8ttRYQXzG3FxHFYcKLF20hoZ12/Hvw0cMHvpVquYGUMlcxwB1OzSgRKVS7FiwxfjcveBwvm0xmCH1v6Z+JyfyFny9AdYLcyZnHSf5Wgx9Tp48wwc1WtCwfnuGDe9H1qxZAIiNjaVubWfKlalD9eqVKVe+zGtkTPr/+OV9Xj6vhYUFzs7N2Lhpp/G5BQtWUPb9OlR3bEZwyB1+/mncK2d/cbbkbcehIXeoWsGJRvU6MHb0NOYvmkmu3DmNfXQ6HQsXz2LR/JXc8gtItIzUkJxt5GUKFrJk8txxjBs8+bWq+MmR1PKfX7/D+/fmpM95On8xiJM+F7C2skSn0/FhzarUq+VI9/4jGTHhZ6pUeB+dTpemeTOq5GzXL9K5VweOeBw1GRynudfIa1fclqKlitK1Znc+rdENhzoOVPqgYmonTEIy3kd0OnR2hfln9CAezJhAzgEjUDlzARAbHkbUoM+J/LorWZ1aoPImLmJlBLFoaf5ID2/bObcD1f/Zu++oKK42juPfuwvYG1aKHY3RqBh7ib0ramJLYo9GozHG3mOvb4wtdo3dWGJiBQv2HntFERSU3tEkNljm/WMRWcGCsoLyfM7xZHfmmdkfN8PO3bt3BqU+j32cHyj23Pp6QHngTOwvfwbg6W/wE2B37OMrwGNN06KUUleAQvH24appWhiAUuovoAZwNv6LaJq2BFgCMK1gxyT9n/knMJwsNs9GEbPYWPNP0ItHBt22n6LhpG4A2Do6UKJJJeqM+JJ0WTOiaRrRj6M4v8o1KRFei79/IHZ2NnHPbe3yERho+mbo7xeInX0+/P0D0ev1ZM2WmYjwSMpXKEuLlo0ZN3Eo2bJlJSYmhkePHrN96278/QI5FzuSuG3bbrN0bsMDw8hp8+wTdk6bnEQEhSeo+6R6GVr1bcOEdqPjpiLEFxEcge9NHz6qVJLTLsk/MufvF4i9ffw2tiHg+Tb2N9bEtXHWLISHR5rU3HS/xX//PaBkyY+4cOFK3PJ79/7h2NG/qd+gJtfdbr5RRj/fAOztn13QaGdng39AUIKa/Pa2+PkFoNfryZYtK+HhEfj6Jdw2IN4oZOPGdbhw4QrB8S4oiv/4t9/WsXXrqjfK/ZS/XyB28UaLbe3yJjiOA2KP9QD/oLg2jogwtvGTJ8b/Xr54DW+vuxR1KMylC1cBmDlnIrdvebN44dtlfJkg/xDy2T4bac5jk5vgwNCXbGEqU+aMzFs7g3nTl3Dl/DVzRDSRN3cukwvEgkJCyZ3L9FuTPLlyMmeycTbZgwcP2XfkBFkyGz809Orcjl6djRcbDZ0wg4L2phfTCqPggBDy2uaJe57XJjehr3lclClfCsfKZWjTpRUZM2XAwtKSh/89ZN6U5JnnnpjQgFBy2z4bWc5tk4vwRN6TE1O9UXVuXLjBowePADhz8AwlypXgyt9XzZL1KS0sBH2uZ22sy5mbmHDTNo4JCyHa3Q0MBmKCAzH4+aCzscfgeePZfsLDMPh4Y1GqDFEnDps1s3h9qWbkVilVG6gPVNU0rSxwAXj+aiQFrIodyXXUNO0jTdPGxa6L0p59VIwBHgNomhaDaSf++c5qsn6sCLh0G+vC+ciWPzc6Sz0lnarg6Wo6ryxHoWcnM4e6jkR4G79WXdd2IgtrDGBhjQGcXb6Hk/O3m6VjC3D+3BWKFC1EgYL2WFpa8kXrZux23m9Ss8tlP19+bbwTQctWjTl6+BQAzRp9jeMndXD8pA6LFqxk1i+LWLZkLcHBofj5BeBQrDAAtWpVNbl4KrncuuRBvsI25M6fB72lBVWdanDO9bRJTaFShekxtQ8zuk/hfti9uOXW+XJiGTsCmilrJj6qUIKAW/6Yw7lzlylStBAFY9u4dZvmuDjvM6lxcd7PVx2MF1i0+rwJh2O//i5Y0D5uVCt/fluKFS/Cnbu+5MxlTbZsxrmB6dOno3ad6ni4337jjGfOXsTBoTCFCuXH0tKS9u1asnPnXpOanTv30qlTWwBat27GwUPH45a3b9cSKysrChXKj4NDYU6fuRC3Xfv2rRJMSciX79nJpFXLJly75v7G2QEunL9C4XjHcasvmrHb5YBJzW6XA7T/2viZ2alVI44dMR7HOXPmQBc7V65gIXuKFC3EHW/j16EjRvcna7bMjBpu3mtZr128ToEi9tgVsMHC0oLGrepzeO+x19rWwtKCWSumseOPXbjuOGjWnE99UqIYd3398fUPJCoqil37j1Knuul894jI+3Hzg5eu28znTesDxikhkffuA+B+y4ubt7yp9twcbWHkdvEGBQrbY5vfeFw0aFmPI3uPv9a2P/WdiFPFtrSs3J45ExbgsnmPWTu2AO6X3LErZEu+/HmxsLSgVotanHQ99VrbBvsHU7pyaXR6HXoLPWWqlH4n0xKiPW6gs7FHlycfWFhg9Vldok6btnHUqWNYljYeoypLNnR2+YkJ8kflzA1WxvOIypQZixKfEONn/szmIHdLML9sQISmaQ+UUiWAKrHLo5RSlpqmRQH7gW1KqVmapgUrpayBLJqm3XnRThPRIHa7h0Ar4Jvk/CE0Qwx7x6yi/eqhKL2Oy5sOE+rhx2cDWxNw2QvPfecp36UhBWuUIibKwKP7/+E80LxvPIkxGAwMHTyezVuXo9fpWbdmMzdueDJi1I9cuHCF3S4HWLv6DxYtncHZi/uIiIikR+wV5i8zbPBEFi/7BSsrS7y9fejbe3iyZ48xxLByzFJGrB6LTq/n0KZ9+Hr40GbgV3hd9uTcvjN8PbIr6TOm58cFQwEI8w9hRo8p2DnY03F0NzRNQynFziXb8HFPyuHz+gwGA0MGjWPLtlXo9TrWrP6DG9c9GDW6P+fPX2GXy35Wr9rIkmUzuXj5ABER9+jWpR8AVatVYMDA74iKjiYmJoaB/ccQHhZBqU9KsGjJz+j1enQ6xZY/Xdi9+8Arkrw844/9R+Ps/Dt6nY6Vqzbi5naTsWMHc+7cJXbudGX5ig2sXDmX627HiIiIpEPHPgC4ud3kj807uHzpINEGA/1+HBXXqcmQIT3169WkT59hJq83bepoypYtiaZpeN/xTbD+TfKPGDyBTX8tQ6fXs37tn7jf8GTYyH5cvHCVPbsOsG7NZhYs+ZnTF/YSEXGPnt8Yj+Oq1SsybGQ/oqMNxMQYGDxgLJER97CxzcvAIb256X6LA0eM01l+W7qWtasT3vbqbRkMBqaOnMnC9bPQ6fVsXb+TW+5e9Bnag2sXb3B47zFKOX7MrOVTyZo9C7Ua1KDPkO58UasjjVrU49MqjmTLkZUW7Y23DRzz42TcryV1KtXrs7DQM7J/L3oNHochJobPm9bHoXAB5v22jlIfOVCnRmXOXLzC7MWrUUpRvmwpRg8w3l4rOtpA574jAMicKQPTRg/EwiJ1TEsYMnYaZy5cJjLyPvVadaRP9060djLvbQJfxmAw8L9Rs5n7+wz0eh3bN7hw+6Y3vYZ8w/VL7hzZe5ySZUvwv98mkTV7Fmo0qEavwd/Qvk6XFMkbY4hh3k8LmLJ2Mjq9jj0b93Ln5h06D+rEzcsenHI9RfGyxRm79CeyZMtClfqV6TSwEz3r9+Ko8zEcqzmyxHURmqZx9vA5Tu37+9Uv+vahebBkNlnGzQCdjsf7XTD4eJPh62+I9rxB1OkTRF04jWW5imSbtwrNEMPDlQvR/rmPRdkKZPymz9P5WTzauhHDnTcfZBDJT5l7jtbrUkqlA7YCdoA7kBsYBzQBWgDnY+fdtgdGYBx1jgK+1zTtlFLqX03TMsfuaxzwr6ZpM2Kf/6tpWmalVFegKcb5uQ7A75qmjX9ZrqROS0hp/wt/B28KyayRdamUjpBkzqFmvk1NMnsYZf553Mkte4bMKR0hyWwzvN5FNKnF2UsrUjpC0uktUzpBklUr0zWlIyRJDn3K354tqTZ8+vDVRamM9bbD5rtdz2v6sdCXZu/jzPHe8M5/zlQzcqtp2mOMHdnnHQKGxavbCGxMZPvM8R6Pe9E6IFjTtL5vGVcIIYQQQqRCqaZzK4QQQggh3h0txWbFmlea6txqmrYSWJnCMYQQQgghhJmkqc6tEEIIIYQwSqm/IGZuqeZWYEIIIYQQQrwtGbkVQgghhEiDUuoviJmbdG6FEEIIIdKgD7NrK9MShBBCCCHEB0RGboUQQggh0qAPdVqCjNwKIYQQQogPhozcCiGEEEKkQXIrMCGEEEIIIVI5GbkVQgghhEiDPtQ/vysjt0IIIYQQ4oMhI7dCCCGEEGnQhzrnVjq3r3BSi0zpCElik9E6pSMkWfR7+LXIE0N0SkdIkuwZMqd0hCR7EPU4pSMk2e7S6VI6QpJUKvtNSkdIstNX16R0hCQ7cXllSkdIsp4VhqR0hCSpciI0pSMk2c2UDvABk86tEEKI90a1Ml1TOkKSvI8dW5F2yJxbIYQQQgghUjkZuRVCCCGESIM+1Dm3MnIrhBBCCCE+GDJyK4QQQgiRBsVoMudWCCGEEEKIVE1GboUQQggh0qAPc9xWRm6FEEIIIcQHREZuhRBCCCHSoJgPdOxWRm6FEEIIIcQHQ0ZuhRBCCCHSoA/1L5RJ51YIIYQQIg2SP+IghBBCCCFEKicjt0IIIYQQaZBcUCaEEEIIIUQqJyO3QgghhBBp0Id6QZmM3JpBuVqfsuDgIhYdWULrPm0SrG/RoxXz9i9gzp5fmbB+MrntcsetG7t6POuubGD0ijHvMjI16lRh5/FN7Dq1mR4/dE6wvnwVR/5wXcUlv+M0bF7XZN3i9bM5eXMf89f+8q7i4lirHHMOLODXw4to1bt1gvXNe7Rg1r55zNg9hzG/TyBXbBsXKlmYyVumM9P1V2bsnkO15jXMmrNBg1pcvnyQa9eOMHhwnwTrraysWLNmPteuHeHIkW0ULGgPgLV1dvbs2UBo6HVmzZpgso2lpSXz50/jypVDXLp0gFatmiRb3rr1PuPk2d2cvrCXfgO+TSSvJUtXzOL0hb3s3r+J/AXsAMhfwI67gZc4eHQrB49u5edZ4wHIkCE9v29azIkzuzh6aic/jRuUbFmfatCgFhcu7ufylUMMGtQ7kcxWrFo9j8tXDnHo8FYKFDC2cd26NTh2fAenT+/m2PEd1KpVNcG2m/5Yypkze5I9c3zpKlck9++ryL1hLZk6fpVgfYYmjcizYwu5Viwl14qlZGjeNG5dlt49ybV6OblWLyd93TpmzRlftTqV2XJsPdtObqRb344J1n9apSy/713OGd/D1G9eO265jX1e1u35jQ37VrL58FradG71TvJWrV2JzUfX8tfx3+nSt0OC9eUql2XNnmWcvHuAus1qJVifKXNGnM/9yZDJ/d9F3FcaPWUmNZt9SauO36V0lDif1HJkyv65TDs0j6a9P0+wvmF3Jya5zmbCrpkMWTeWnPHOe7/d2sR4lxmMd5lBv6XD32XsOJ/Vrcruk3/ienoLPft1SbC+QtVybNm/FreAUzRyqpcCCUVSvHcjt0qpQsBOTdM+SeEoidLpdPSa1JuxHUYTFhDGjB2zOO36Nz4ePnE1XtduMbDZAJ48ekzjjk3oOrIbP3//PwC2LP6LdBnS0ahD43eaedS0IXzb7geC/IPZuGclB/cc5dZNr7iaAL8gRv04ka69E54Yli9YS4YM6WnbOeEbmrnydp/Yi4kdxhIeGMbU7TM4u+80viZt7MWw5gN58ugJDTs2ptOIrszq+zOPHz7m1wGzCfQOIEcea6Y7/8LFIxd4cP8/s+ScM2cSzZp1wNc3gOPHd7Bzpys3bnjE1XTt2p7IyHuUKlWTtm2dmDRpBJ06fc+jR48ZP/4XSpb8iFKlipvsd/jwHwgJCaV06doopbC2zp5seaf9Moa2rbrh7xfE3oOb2e1ygJvut+JqOnRuS2TkfSqVa0ir1k0ZM34w33YbAIC3113qfJawszL/1+UcP/o3lpaW/LV9JfXq12T/viPJlnnmrAk4Ne+In18gR49ux9nZlRs3PONqunRtR2TkPcqUrk2bNk5MnDScLp37EhYWQZs23QkMCKZkyeJs276aYg5V4rZr0bIR//37IFlyvuQHIOvAHwkfMARDcAi5li3i8bETRHvfMSl7dOAg92fNNVmWrmoVLIsXI7RbD5SlFdbzZvP41N9oD8ybWafTMXzqIHq3609QQDDrdi/j8N5j3L7pHVcT4BfE2B8n07mPaWc9JCiMrk7fEfUkigwZM7D58BoO7zlGSFCoWfMOnTKAvl8OJCgghFUuSziy5xheHs/aONAviPH9p9Dxuy8T3cd3Q3tw/tRFs2VMqlZNG/B16xaMnDgjpaMAoHQ6Ok34lhkdJxAeGMaY7dO56HoGf0/fuJq7bl5McBrKk0dPqNOxEe1GdGJh35kAPHn0hLFNB6dUfHQ6HWOnDaNb2+8J9A/iz72r2b/7iOk50DeQ4T+Mo3ufTimW0xzkbgnitRRzLE6gdwBBd4OIjorm6I4jVGpYxaTmyskrPHn0GAD3C+7ktMkVt+7y8Us8/PfhO81c+tOS+Hj54nvHn6ioaFy2ulKncU2TGn+fAG66eaLFJPxV+PvoWfN3AuJxcCxGoHcgwT7GNj6+4ygVGlQyqbl28gpPHj0B4OYFd6xtcgIQ4OVPoHcAABHB4dwLvUdW66xmyVmxoiO3bnnj5XWXqKgo/vhjB05ODU1qnJwasnbtZgD++suFOnWqA/DgwUNOnDjD48ePEuy3S5d2/O9/8wHQNI2wsIhkyftp+TJ4377DHW9foqKi2PqXM02amY5QNGlal42/bwFgx9Y9fJbIaGd8Dx8+4vjRvwGIiori8iU3bOzyJktegAoVHLl96w7e3j5ERUWxefMOmjc3bePmzRqybu2fAGzZ4kLt2tUAuHTpGoEBwQC4ud0kXbp0WFlZAZApU0Z++KEH06f/mmxZE2P5cQkMvv4Y/AMgOpqH+w6Qrkb119rWolBBnly8BIYYtEePiPa8RboqlV694Vv6pNzH+Hj54nfXn+ioaPZs3U/tRp+Z1AT4BOJx/RYxMaZfeUZHRRP1JAoAq3SWKKXMnrdUuY/x8fbD724A0VHRuG7bT61Gpt/YBPgG4nn9NlpMwq9oS5QujnXuHPx9+IzZs76uCo6lyZY1S0rHiFPE0YHgO4GE+ARhiIrm9I5jlGtY0aTmxsmrce/Jty7cJEe+nCkRNVFlPi3FHW8ffO74ERUVjfPWvdRvYjqC7+cTgLubJzHah9od/LC8r51bvVJqqVLqmlJqr1Iqg1LqkFKqAoBSKpdSyjv2cVel1Fal1A6llJdSqq9SaqBS6oJS6pRSyjo5g+XMl5NQ/5C452EBoeTM++Jf4gbtG3Lu4LnkjJBkefPlIcA/KO55kH8wefPlfskWKcs6X07CAp6N9IQHhJHzJW+U9do34MKhhG3sULYYFlYWBN0JNEtOW9t8+Pr6xz338wvA1jbvC2sMBgP37/9Dzpw5XrjPbNmMHfGxYwdz8qQz69YtJE+eXC+sTwob27z4+T1rC3+/IGxsTPPms8mLn1+ASV5ra2PeAgXtOXB0C9uc11ClavkE+8+aLQsNm9Th6OGTyZIXwNY2L75+pm1sk6CNn9W8qI1btWrC5UvXePLEePIdM2YQc+cu48GDhB8ukpM+dy4MwcFxz2NCQtDnTvj/M32tmuRauYzsE8ehy2P83YzyvEW6ypUhXTpUtqxYfeqIPo/5f2/z2OQmyP9Z5qCAYHLbvP7r5rXNw8YDq9h1bgsr568z66gtQO58uZ7LG/LaeZVS9B/7PXMnLjRXvA9CjrzWhPvHf08OJ8dLzns129XjyqHzcc8t01kxZvt0Rm+ZSrmG5v+A9ry8NnkI9Ht2Dgz0DyavTZ53niMlaJpm9n8p4X3t3BYD5muaVgqIBBJOujT1CfA1UAmYDDzQNK0ccBJIMMFUKdVTKXVWKXXW+9+7SUuWyEDEi/7n1vq8Ng5lHNiy+M+kvUZySyzzezbJ/EVt/NnntShS2oHti7eYLM+eJwc/zBrAgsFzzfbLl9io1POv9To18VlY6LG3t+XkybNUrdqMv/8+x7Rpo98+7GtmeVFNUGAw5UrVoe5nn/PTqGksWvYLmbNkiqvR6/Us+W0myxat4Y63b4J9mDMzr6j5+ONiTJw0nB9+GAlAmTIlKVK0IDu2m3eu7Yuy8Vz+R8dPEtz2K0K79uDJ2XNkH2Wck/jkzFkenzpFrkXzyDHuJ6KuuqEZ3sGo0mtkfpkg/2Da1+1Cy6rtcWrXBOtcL/4wlxyS+jsWX5uun3P8wCmTzrFIRBLauGqrmhQqU5RdS7bFLRtcrRcTWgxjcb/ZfD2mG7kLJN+3O68j8UP6/ToHClPva+fWS9O0pxOgzgGFXlF/UNO0fzRNCwHuATtil19JbFtN05ZomlZB07QKhTIXSFKwsIAwctk+GxXIaZOL8ODwBHVla5Slbd/2TO4+kegn0Ul6jeQWFBBsMtqV1zYPwYHmHU15G+GBYSZTOaxtchIelLCNS1cvyxd92zK9x2STNs6QOQMjVvzE+hlr8bhw02w5/fwCsLe3jXtuZ2dDQEDwC2v0ej1Zs2YhPDzyhfsMC4vgv/8esG3bbgD++ssZR8fkmX7u7xeInV2+uOe2dnkJDDTNG+AfiJ2djUneiIhInjyJIiLCmPvyxWt4e92lqEPhuO1mzpnI7VveLF64KlmyPuXnF4i9nWkbBz7Xxv7xap5vY1u7fKzfsJhvewzEy8v4QbZS5U8pV640btePsW//HzgUK8yu3RuSNfdThuAQ9HmejRDpcufGEBpmUqPdvw9Rxq/yH+xwxvKjZ3Ow/129jtBu3xI+YAgohcEn+T44vEiwfzB5bZ9lzmuTh5A3eL8ICQrllrsXn1Ypm5zxEggOCHkub25CXzNvmfKlaNftC7b9vZEfx/ShaZtG9B3Zy1xR31sRgWFY28Z/T7YmMpHzXsnqZWjetzVzekw1eU+ODDZOrQrxCeLGqWsULFU4wbbmFOgfTL5406Xy2eYhODDkJVt8OGLQzP7vVZRSjZVS7kopT6VUgisKY79td1NKXVZK7VdKFXzVPt/Xzu3jeI8NGC+Mi+bZz5P+JfUx8Z7HkMwX1XlcuolNYVvy5M+LhaUFnznV5LTr3yY1hUsVoffUvkzuPpF7YfeS8+XfyNUL1ylQJD92BWywtLSgaasGHNyTPBf8mIPnJQ9sCtuQJ38eLCwtqO70GWddT5vUFCpVmJ5TezO9+2Tux2tjC0sLhiwZweE/D3LK5YRZc549ewkHh8IUKpQfS0tL2rZ1YudOV5OanTtd6djReEeNL75oyqFDr87k7Lwv7sr+OnWqc/26xyu2eD0Xzl+hcNFCFChoj6WlJa2+aMZulwMmNbtdDtD+a+OFg06tGnHsyCkAcubMgU5n/PUrWMieIkULccfbeIHfiNH9yZotM6OGT0mWnPGdO3eJog6FKBibuU0bJ5ydTdvY2cWVDh2NX+58/nlTDh82tnG2bFn5688VjB3zP06dejZtZdnStTgUrUzJj2tQv15bPD28aNI48QuN3lbUjRvo89uht8kHFhZkqF+Xx8dNjwFdzmczp9LVqEb0ndhvk3Q6VFbjNBWLokWwKFqEx2fMPy/02sUbFChij20BGywsLWjUqh6H9h57rW3z2OQmXXrjvOYs2bLgWLE03p5J/HYsidwu3qBAYXts8xvzNmhZjyN7j7/Wtj/1nYhTxba0rNyeORMW4LJ5D/OmLDZr3veR1yVP8hSyIZd9HvSWFlRyqsEF17MmNQVKFabLlF7M7TGNf8Luxy3PmDUTFlbG03DmHFkoVr4E/h7m/5AW35ULbhQqnB/7ArZYWlrQrFVD9u9OvefAD4lSSg/MB5oAJYGvlFIlnyu7AFTQNK0MsBn436v2+97dLeElvIHywGkg4f233pEYQwxLflrEuDUT0Ol17N/ois/Nu3w9sAOeVzw47XqabqO+IUPG9AxdaPyAEuofwuTuEwGYsnk69kXtSZ8pPb/9vZJ5Q+Zy4cj5l73kWzMYDEweMYMlG+ai0+vYsn4Ht9y96Du0J9cuXefgnqN84vgxc1b8j6zZs1C74Wd8P+RbWtYyXgm9ettiCjsUJGOmDOy/sIMxAyZx/NDfr3jVNxdjiOG3MUsYtXocOr2Og5v24+vhQ/uBX3Prsidn952m08hupM+YgUELhgIQ6h/K9B6Tqdq8Oh9XKkWW7Fmo08Z4S7P5g+fi7eb1spd8IwaDgf79f2LHjjXo9XpWrdrI9es3GTNmIOfOXcHZ2ZWVKzeyfPlsrl07Qnh4JJ07943b3t39OFmyZMHKyhInp0Y0b97j2iZJAAAgAElEQVSRGzc8GD16KsuXz+bnn8cSGhpOz57Jc3stg8HAiMET2PTXMnR6PevX/on7DU+GjezHxQtX2bPrAOvWbGbBkp85fWEvERH36PmN8U4JVatXZNjIfkRHG4iJMTB4wFgiI+5hY5uXgUN6c9P9FgeOGKeG/LZ0LWtXb062zIMGjmHb9tXo9XpWr97E9esejP5pAOfPX8HFeR+rVm5i2W8zuXzlEBERkXTp/AMAvb7rTJGiBRk+oh/DR/QDoIVTJ0JCwl72ksnLEMP9mXOxnvk/0Ol46LyLaC9vMnfvRtQNdx4fP0GmNl8YLzIzGIi5f5/IydOM21royTl/DgDagwdETpgM72BagsFgYPrIWSxYPxOdXs+29Tu57e5F76E9cLt4g8N7j1HSsQQzl08la/Ys1GxQne+G9KBNrY4ULlaIgeP6GqcxKMXqhevxvHHb7Hn/N2o2c3+fgV6vY/sGF27f9KbXkG+4fsmdI3uPU7JsCf732ySyZs9CjQbV6DX4G9rXSXg7qNRiyNhpnLlwmcjI+9Rr1ZE+3TvR2qlRiuWJMcSwbswyBq3+CZ1ex9FNB/D38KHVgC/xvuLJxX1naTeiM+kypqfPAuP7VZhfKHO/nYatgz1dpvQiRtPQKYXzwi0md1l4FwwGAxNG/Mxvm35Fr9Ozef12PN1v029YL65evM6BPUco7ViS+at+Jmu2rNRp+Bn9hvak2Wft32lOc0gFl8dVAjw1TbsNoJTaALQE3J4WaJp2MF79KSDh/Qefo963eSXP3wpMKTUYyAxsADYB/wIHgI6aphVSSnXF2OPvG1vvHfs89Pl1iWlZoPl71UCej9+/r1JKps/36qJUZkfQhZSOkCRZ0mVI6QhJ9iDq8auLUhnPT9/t16lvq6nnk5SOkGQWOn1KR0iSE5dXpnSEN9KzwpCUjpAkx/+78+qiVOZmyFnz3y7kFZzeQR9np49zL6BnvEVLNE1bAqCUagM01jStR+zzTkDlF/XLlFLzgEBN0ya97DXfu5FbTdO8MV4g9vR5/Bv9lYn3eHTs+pXAynj1heI9NlknhBBCCJFWvIuLx2M7sktesDqxDn6ioZRSHYEKQMK/tPKc965zK4QQQgghPgi+QP54z+0B/+eLlFL1gVFALU3TXvm1nnRuhRBCCCHSoNe5m4GZnQGKKaUKA37Alxhv3RpHKVUOWIxx+sJr3Zfvfb1bghBCCCGEeI9pmhYN9AX2ANeBTZqmXVNKTVBKtYgt+xnjtVV/KKUuKqW2v2q/MnIrhBBCCJEGpYabCmia5gK4PLdsTLzH9ZO6Txm5FUIIIYQQHwwZuRVCCCGESINSwX1uzUI6t0IIIYQQadC7uBVYSpBpCUIIIYQQ4oMhI7dCCCGEEGlQKrgVmFnIyK0QQgghhPhgyMitEEIIIUQalBpuBWYOMnIrhBBCCCE+GDJyK4QQQgiRBsmcWyGEEEIIIVI5Gbl9hYzq/Woij0i/lI6QZMXz5knpCElmiDGkdIQkuf/4QUpHSDKFSukISVbyok9KR0iST7IVTOkISZZBZ5nSEdKEJWd/TukISdat/OCUjvDe+VDvc/t+9dyEEEKI90jPCkNSOkKSvY8dWyHik86tEEIIIUQaFCN3SxBCCCGEECJ1k5FbIYQQQog06MMct5WRWyGEEEII8QGRkVshhBBCiDRI7nMrhBBCCCFEKicjt0IIIYQQaZCM3AohhBBCCJHKycitEEIIIUQapMl9boUQQgghhEjdZORWCCGEECIN+lDn3ErnVgghhBAiDdI+0M6tTEsQQgghhBAfDBm5FUIIIYRIg+SCMvHaytYqxy8H5jPr8EJa9P4iwfqmPVrw875fmb57NqN+n0Auu9wA5LLLzeSdvzDVZRY/u86lfodGZs3ZsGFtrl45jJvbMYYM/j7BeisrK9atXYCb2zGOHd1BwYL2AFhbZ2fvnk2Eh7kze/akuPoMGdKzdesqrlw+xMUL+5k8aYTZsper9SnzDi5kwZHFfNGnTYL1LXq0ZO7++czaM5fx6yeRO7aNAX5aPY61V9YzasUYs2Rr2LA2V68e4brbMYYMeUG7rlvIdbdjHD/2rF0Bhg7ty3W3Y1y9eoQGDWrFLc+WLSsbNizhypXDXL58iCqVywMwbtwQzp9z5eyZvbg4/46NTd63z9+gNlcuH8Lt2lEGD+6TaP61axbgdu0oR49sNzku9uzZSFjoDWbPmpjovv/cvJzz5/a9dcb4GjSoxeXLB7l27cgL865ZM59r145w5Mi25/JuIDT0OrNmTTDZpk0bJ86c2cP58/uYPHlksuZ9Xr36NTl9fi/nLu2n/8Beieb/bdUczl3aj+vBzeQvYGey3t7eBp/AS/Tt192sOZ+qVLsi646sZP2x1XT4/ssE68tWLs1vuxdx8M5eajerabIuj20efvl9OmsOLWfNweXks3/74/V1VKhdnt8OLWPF0eW079MuwfrSlT9hvss8dnk581nTGibreozszpJ9i1l2YAl9xvd+J3kBPqnlyJT9c5l2aB5Ne3+eYH3D7k5Mcp3NhF0zGbJuLDnjvcf9dmsT411mMN5lBv2WDn9nmV9m9JSZ1Gz2Ja06fpfSUeKUqVWOnw/8yi+H5+OUSBs36eHE9H1zmLJ7JiN+HxfXxjntcjNx589MdvmFaa6zqduh4buOLl4h2Tu3SikXpVT2JNQXUkpdTe4cr/na/yb7PnU6uk3sxfQuExhc/weqtfgMu2L2JjXe124zqvkghjXuz98uJ/h6RBcAIoIjGPvFMEY0HcDolkNp0bs1OfLkSO6IAOh0OubMmYRTi06ULVuH9u1b8nGJYiY13bp9SUTkPUqWrMHcuUuZEnuSf/ToMePG/8yw4Qk7MLNmLaZ0mdpUrNSYqlUr0KhRHbNk7znpOyZ2GUe/et9To0VN7IvlN6m5fe02g5sNZECjfpxwPk7nkd3i1m1d/BezB8xM9lxPs82dMxknp46UKVuHL9u34uOPTdv1m25fERlxj49L1mDO3KVMmTIKgI8/Lkb7di0p61iX5s078OvcKeh0xl/RWTMnsHfPQUqXrkX58g24fsMDgF9+Wcin5RtQoWJDXFz2MXrUgLfOP2fOJFq07ExZx7q0b9eSEs8fF12/JDIykpKlPmPur8uYPOnZcTF+/AyGD5+U2K5p2bIx//7331vle1Heli274OhYj3btWiTI27VreyIj71GqVE1+/XUZk2I/dBnz/sLw4ZNN6q2tszN16kiaNPmKTz+tT968uahTp3qy5o6f/+eZ42j7RXeqVGhM67bN+aiEg0lNpy5tuRd5j/Jl67Fw/grGTRxqsn7y9FHscz1ilnyJ5R04uR+DO46gU51vqN+qLoWKFTSpCfILZsqA/7Fv6/4E24+eM4z1CzfRqfY39GzWh4jQyHeSue+k7xnVeTTf1u1J7Za1KVCsgElNsF8IMwb+woGtB02Wlyz/MaUqlOS7hr3pWf87ipctTpkqZcyeWel0dJrwLbO6TmZUg/5UblEDWwfT88hdNy8mOA1lTJOBnN11inYjOsWte/LoCWObDmZs08HM/Xaa2fO+jlZNG7BoZuLvDSlB6XR0mfgt/+syiaH1f6RKi8+wTXCu9uKn5kMY2Xggp11O8tWIzgBEBkcw/osRjGo6iLEth+PU+wuym+lcbW4xaGb/lxKSvXOraVpTTdPM/46VSjk4FiPQO4BgnyAMUdGc3HGMCg0qm9S4nbzKk0dPAPC84I61TU4ADFHRRD+JBsDSyhKlU2bLWbGiI7dueePldZeoqCg2bdqGk5Ppp08np4asWfMHAH/+5UydOsYRjQcPHnLixBkePXpsUv/w4SMOHz4BQFRUFBcuXsXOzibZsxdzLEaAdwBBd4OIjorm2I4jVGpo2sZXT17hSWy+mxfcyRnbxgBXjl/m4b8Pkz0XQKWK5UzadeOmbTg5mY7Am7Trn87UjW1XJ6dGbNy0jSdPnuDt7cOtW95UqliOLFkyU6NGZZavWA8Y2/bevfsA/PPPs89nGTNlfOuvmBIcF39sT/y4WLsZgL/+co7r+MUdF48fJ9hvpkwZ+fHHb5k6de5b5XtV3j/+2JFo3rVxeV0S5H38+JFJfeHCBfDw8CI0NByAAweO0apVk2TN/VT5CmW5ffsOd7x9iIqK4q/NzjRtVt+kpkmz+qxftwWAbVt2U6t21bh1TZvX546XDzeue5gl3/M+LlcCP28/Au4GEB0Vzf5tB6nRqJpJTaBvELeu30aLMT0WCxUriN5Cz9mj5wB4+OARjx8lPFaS20eOH+HvHUDg3UCio6I5vP0w1RpWNakJ8g3C64ZXgt8fTQOrdFZYWFlgaWWJhaWeiNAIs2cu4uhA8J1AQmLPI6d3HKNcw4omNTfinUduXbhJjnw5E9tVqlHBsTTZsmZJ6Rhxijo6EOQdENfGp3Yco3yDSiY1103O1TdfcK62MOu5WryZJHdulVJDlVL9Yh/PUkodiH1cTym1VinlrZTKFTsie10ptVQpdU0ptVcplSG2trxS6pJS6iTwfbx9l1JKnVZKXVRKXVZKFYvdzw2l1KrYZZuVUhnj7eewUuqcUmqPUsomdnlRpdTu2OVHlVIlYpcXVkqdVEqdUUol/r3pW8qRz5qwgNC452EBYeTIZ/3C+trt63Pp0Pm459Y2uZi+ezbzTi1j+6K/iAg2zxupna0Nvj4Bcc/9/AKxfa4jamebD19fY43BYODe/fvkzPl6n06zZctKs2b1OXjwWPKFjmWdLyeh/qZtnDPvi9/Y67dvwPmD55I9R2Js7fLh6+sf99zPLwA723wJanxiawwGA/fuGdvV2N6m29ra5aNIkYKEhobx27JZnDm9h8WLfiZjxgxxdRMmDOP2rTN89dXnjBv/89vlt32W7YX54+U0GAzcv//PK4+LcWOHMHv2Uh4+TN4PFbaJtZlt3hfWvE7eW7fuULx4UQoWtEev1+Pk1BB7e9tkzf2UjW1e/Hyf/R76+wVikyD/sxqDwcD9e/9inTMHGTNm4McBvZg+9VezZEtM7ny5CPYPiXseEhBCrny5Xmvb/EXs+ff+f0xaOo7f9iyiz+iecd9MmFOufDkJMckcSs7X7AheP3+diycvseHs72w49ztnD5/Dx9PHXFHj5MhrTXi897jwgHByvOQ9rma7elyJdx6xTGfFmO3TGb1lKuUaVnrhdmlZjnw5CQ8Ii3se/opzda329Z47V+dkyu6ZzDm1lJ2LthBppnO1uWmaZvZ/KeFN3lmOAJ/FPq4AZFZKWQI1gKPP1RYD5muaVgqIBFrHLl8B9NM0repz9d8BczRNc4zdt2/s8o+AJZqmlQHuA31iX/NXoI2maeWB5cDT7xeXAD/ELh8MLIhdPgdYqGlaRSDwDX72V1Ik8gnuBf9va3xeiyKlHdixeEvcsvCAUIY17s+Amt9Rs3UdsuXKZo6YqMRiPncQqkSKXudA1ev1rFkzn/nzl+PldfeNM75IUnLV+rw2Rcs4sHXxX8meIzGvky3xmhdva6HXU65caRYvXk3FSo34778HDB3aN65mzJjpFClakfXrt9CnT7cE+0j+/Am3e9lxUaZMSYoWLcj27bvfKlti3ry9X5w3MvIe/fqNYs2a+ezfv5k7d3yJjo5++7CJeK1sL6gZPupHFs5fwX//PTBLtkQlNkD1micvvYWeMpU+Yf7ExfRs2gebAjY0aWfe6wqAF7bf67AtZEMBhwJ8XakjX1XsgGM1R0pX/iS5EyaUhMxVW9WkUJmi7FqyLW7Z4Gq9mNBiGIv7zebrMd3IXeDdzG1+nyQ61vqCw6L65zUpUtoB58Vb45aFB4QxsvFABtXsw2et65DVTOdq8WbepHN7DiivlMoCPAZOYuyIfkbCzq2XpmkX421XSCmVDciuadrh2OVr4tWfBEYqpYYBBTVNezrM46Np2vHYx2sxdqQ/Aj4BXJVSF4HRgL1SKjNQDfgjdvli4OmQZHVgfSKva0Ip1VMpdVYpddbzX+9Xt0g84YFh5LR5NpKR0yYnEUHhCeo+qV6GVn3bMKPHlLivN+KLCI7A96YPH1UqmaTXf12+fgHY5382Umtnl48A/8CENfbGGr1eT7asWQkPf/WMk4ULpuPp6cWvv/6WvKFjhQWEksvWtI3DgxO2cZkaZWnTtx1Tu09KtI3Nwc83wGSUz87OBv+AoAQ1+WNr9Ho92bJlJTw8Ira9TbcN8A/C1y8AX98ATp+5ABiniJRzLJ3gtTds2MLnnzd9u/x+z7K9ML9fYFxOvV5P1qxZXnpcVKlcnnLlyuDufoID+/+iWLHC7N276a1yxs+boM0Cgl9Y8zp5AVxc9lGzZktq1/4cD4/beHp6J0ve5/n7BWJn/+z30NYuH4HP5Y9fo9fryZotMxHhkVSoWJbxE4dy6dohevfpysDBvfm2VyfMKSQglDy2zy5cym2Tm9CgsJds8UxwQAgeVz0JuBuAwRDDsT3HKV662Ks3fEuhAaHkNsmci/BE3pMTU71RdW5cuMGjB4949OARZw6eoUS5EuaKGiciMAzreO9x1jbWRCbyHleyehma923NnB5TTd7jno4ihvgEcePUNQqWKmz2zO+b8MCwuGkGYByJTexcXap6GVr0bcPM59r4qcjgCPzMeK42N5lzG0vTtCjAG+gGnMDYoa0DFAWuP1cef0KVAeOtxxQv+HykadrvQAvgIbBHKVX36arnS2P3c03TNMfYf6U1TWsY+zNFxlvuqGnax89t+6qfcYmmaRU0TavgkLnQq8pN3LrkQb7CNuTOnwe9pQVVnWpwzvW0SU2hUoXpMbUPM7pP4X7Yvbjl1vlyYpnOCoBMWTPxUYUSBNzyxxzOnr2Eg0NhChXKj6WlJe3atWTnTleTmp07XenUqS0Arb9oxqFDxxPblYnx44aQLVtWBg0aa5bcAB6XPLApbEue/HmxsLSghlNNzjzXxoVLFaH31O+Z0n0i9+K1sbmdOXvRpF3bt2vJzp17TWp27tz7rF1bN+NgbLvu3LmX9u1aYmVlRaFC+XFwKMzpMxcICgrB19ef4sWLAlC3bg2uX78JgIPDs5OWU/OGuLvfeqv8xuOi0LPjom2LxI+LjsY7VHzxGsfFkqVrKFykAh99VI269b7Aw8OLhg0TXrH+5nmftXfbtk6J5u0Yl7cphw6deOV+c+c2nvSyZ89Gz56dWLFi/Su2eDPnz12maNGCFChoj6WlJV+0acYuF9MLsXa77OerDsYruVt+3pgjh08B0LThV5QtVZuypWqzcMFKZs5YyNLFL/zMnixuXLyBfWE7bPLnw8LSgnot63Bs76vb07itO1myZyG7tXGE69Pq5fC+ececcQFwv+SOXSFb8sW+X9RqUYuTrqdea9tg/2BKVy6NTq8zjjxXKf1OpiV4XfIkTyEbctkbzyOVnGpwwfWsSU2BUoXpMqUXc3tM45+w+3HLM2bNhIWV8S6fmXNkoVj5Evh7+CJM3b7kaXKuruJUg/OuZ0xqCpYqzDdTv2Nm96kvPFdnzJqJYhVKEHDL753mFy/3pve5PYLx6/5vgCvATOCcpmlaYl+zxadpWqRS6p5SqoamaceADk/XKaWKALc1TZsb+7gMcBsooJSqqmnaSeAr4BjgDuR+ujx2mkJxTdOuKaW8lFJtNU37QxkDldE07RJwHPgS4+hvB8wgxhDDyjFLGbF6LDq9nkOb9uHr4UObgV/hddmTc/vO8PXIrqTPmJ4fFxiveg7zD2FGjynYOdjTcXQ3NE1DKcXOJdvwcTfPm7/BYKB//59w3rkOnV7HqpUbcbt+k7FjBnPu/CV27nRlxYoNrFwxBze3Y0SER9Kx07PbLN10P0nWrFmwsrKkhVMjmjX7mvv//MuIET9y44YHp/82fgW9YOHKZO8YxBhiWPrTIsauGY9Or2P/xn343LzLVwM74HnFgzOup+kyqhvpM6ZnyELjbXBC/EOY2t14pe7kzdOwK2pP+kzpWfr3CuYPmcvFIxeSJZvBYODH/qNxdv4dvU7HylUbcXO7ydixgzl3ztiuy1dsYOXKuVx3O0ZERCQdOhrb1c3tJn9s3sHlSweJNhjo9+MoYmJiAOg/4CdWr/oVKytLbnvdpUePgcafZfIIihcvihYTw527fnz//dvd9ufpcbFzx1r0ej0rV23k+vWbjBkziPPnLrPT2ZUVKzewYvls3K4dJTw8kk6dn93uzN39BFmzGI8LJ6dGNGvegRs3zHex09O8O3asQa/Xsyou70DOnbuCs7MrK1duZPny2Vy7doTw8Eg6d342pcPd/ThZ4uVt3rwjN2548Msv4yhd2jgSM2XKbDw9vcyWf+ig8fy5dQV6vZ51a/7gxnUPRoz+kYvnr7LLZT9rVm1i0bJfOHdpPxERkXTv2t8sWV4vbwyzRv/KL79PR6fT4bxxF94379B9cFduXHLnuOtJSpT9iMm/jSdLtsxUa1CVbwZ1oXPd7sTExDB/wmJmb5wBCm5e8WDH785mzxxjiGHeTwuYsnYyOr2OPRv3cufmHToP6sTNyx6ccj1F8bLFGbv0J7Jky0KV+pXpNLATPev34qjzMRyrObLEdRGapnH28DlO7fv7nWReN2YZg1b/hE6v4+imA/h7+NBqwJd4X/Hk4r6ztBvRmXQZ09NnwSAAwvxCmfvtNGwd7OkypRcxmoZOKZwXbsHfM+U7t0PGTuPMhctERt6nXquO9OneidZO72BaygvEGGJYNWYZQ1ePQafXcXjTfvw8fGg98Eu8Lt/i/L4zfDWyM+kzpqffgsEAhPmHMrPHVGwd7Pl6dJfY6WTgsmQbvu7JPwXvXfhQ/0KZepPJvkqpesBujNML/lNK3QQWaZo2UynlTexcXGCnpmmfxG4zGMisado4pdTTObIPgD0Y581+opQaAXQEojDOif0ayAq4YOxQVwM8gE6apj1QSjkCc4FsGDvqszVNW6qUKgwsxDgdwRLYoGnahNjlv8fW/gmM1jQt88t+1q8Ktnqv/s//GXj21UWpTLO85VI6QpLtDDz/6qJU5F1cuJPcEp2/nsplsLBK6QhJ8km2gq8uSmUy6CxTOkKS2OkzpXSEJFty9u0uTE0p3coPTukISbL2zl8p/iZXJl9Vs/dxLgeefOc/5xuN3Gqath9jp/Hp8+LxHheKfRiKcU7s0+Uz4j0+B5SNt8txscunAlPjv5ZSKisQo2lagjs/x87nrZnIci+g8QuWx7+ILXXcAFAIIYQQ4h2Lkb9QJoQQQgghROr2pnNu3xlN07yJNwIshBBCCCHe3oc651ZGboUQQgghxAcj1Y/cCiGEEEKI5CdzboUQQgghhEjlZORWCCGEECINkjm3QgghhBBCpHIyciuEEEIIkQbJnFshhBBCCCFSORm5FUIIIYRIgz7UObfSuRVCCCGESINkWoIQQgghhBCpnIzcCiGEEEKkQR/qtAQZuRVCCCGEEB8MGbkVQgghhEiDNC0mpSOYhXRuX6FZVOaUjpAk00qVSOkISfZLeMaUjpBkeTJlT+kIHzzDe/im2zm7Y0pHSJJhpf1TOkKSKV10SkdIkionQlM6Qpqx4tyMlI4gUgnp3AohhBAiTrfyg1M6QpJJx/bNxMicWyGEEEIIIVI3GbkVQgghhEiDNLnPrRBCCCGEEKmbjNwKIYQQQqRBMudWCCGEEEKIVE5GboUQQggh0iCZcyuEEEIIIUQqJyO3QgghhBBpUIyM3AohhBBCCJG6ycitEEIIIUQapMndEoQQQgghhEjdZORWCCGEECINkrslCCGEEEIIkcrJyK0QQgghRBr0of6FMuncmoFN7TJUnNgJpdPhuf4Q1+btSLSuQLOK1Fz6Iy6NfyL8shdWOTJTc0k/cjoW4famI5wZtfqdZU5frSLWg/uAXse/W3Zxf+UGk/WZnBqSo39PDMGhAPyzcRv/bt1FugplsR7UO67OslABQkZM4uGhE2bN+3Gtsnwxpis6vY6TGw+wb+E2k/V1ujej6pd1MUQb+Df8Pr8PXUSEnzF7i+EdKFW3HEqnw/3oZf4cv9JsOWvXq8GEqcPR6fWsX/Mn82cvM1lvZWXJnIVTKe1YiojwSHp/MwhfH3/jz1iqONNnjiVzlszEaDE0q9uex4+f0LJ1U34Y+C2aphEUEMIPvYYRER6ZKvNaWFqwxWVN3PY2tnn5a9NOxo6clix5AerUq8HEaSPR63WsW72ZeYlk/nXRdMo4liQiPJJe3wzE5+6zzD/PGk+WLJmJiYmhcd22PH78hOGjf6Ttly3Jnj0rRe0rJFvWVyleqywtx3RG6XWc3niQQwu3m6yv0qE+VTs1QIuJ4fF/j/hzxDKCPf3eWb6nLD+tRKZvfwCdjkeuzjza/HuCGqsadcjwVVdAw+B1i39nTESXOy9ZRk4EnQ4sLHi04y8e796eYNtkz1uuEhlj8z52debRn4nkrW7Mq2nGvP/NNObNPPxZ3sfO7ybv8z6rW5VRkwej1+v4Y+1WlsxdZbK+QtVyjJo0iI9KOjCg5yj27Nj/zjOWqVWOTmO/QafXcWjDPnYs3GKyvkkPJ2p/WR9DtIF/wu+zZMh8wvxCyGmXm/6Lh6LT6dBb6tm70oUD6/a+8/zPGz1lJkeOn8Y6R3a2rl2U0nHM5kOdlpAmO7dKqUJANU3TEr7Dve2+dYpKU7qw/8tpPAgIp4nLBHz3nOOeh79JnUWm9HzUvREh5zzjlhkeRXHp581k/8ie7CXskzvai+l0WA/7geA+w4gOCsFm7XweHj5BlNddk7L/9h4iYvo8k2WPz14i4KvvjLvJmgXbbat4dOqcWeMqnaLthG+Y33EykYFhDN4+lauuZwmMd5L3dfPmZ6cRRD16Qo2ODWg5ogMr+86h8KfFKVLhI6Y1HgJA/80TcKhSEs9TbsmeU6fTMfnnUXz1+bcE+AfhcmAje3cdxMP9VlzNV51ac+/efWqUb0KLL5owatxAencfjF6vZ+7iaRWgmz4AACAASURBVPz43QjcrrqTI0c2oqKi0ev1TJg6nNpVWhARHsmo8YPo9u3XzJy+IFXmffz4CQ1rto7bftfBTbjsdH3rrPEzT53xE+1adSfAP4jdBzexd9dBbsbL/HWnNkRG3qPqp41p+UVTRo8bTK9vBqLX65m/5H/07TUsNnN2oqKiAdi7+xDLl/7OyXO7ki3rqyid4vMJ3VjacQr3AsP4Yftk3FzPmXReL2w7zql1+wAoWb88Tj914rcuyfdB4bXodGT6rj/3fxpETFgI2WYuJurv4xh87jwrsbEjQ5sO3B/6Pdp//6KyZQcgJiKMe0O+h+goSJ+B7PNW8OT0cbTwMLPmzdirP/+MNebNOmMxT04fJ+a5vOnbdOD+sIR57w97ljfb3HeQN0F8HWOnDaNb2+8J9A/iz72r2b/7CLduesXVBPgGMvyHcXTv0+md5YpP6XR0mfgt0zqMJzwwjAnb/8e5fWfw9/CNq/G+5sVPzYfw5NET6nVsxFcjOjOv7y9EBkcw/osRRD+JJl3G9EzbO5vzrmeIDI5IkZ/lqVZNG/B16xaMnDgjRXOIN5NW59wWAr42x45zlivKP95B/Hs3hJgoA97bTmHfqHyCurJD2+C2YCcxj6PilhkePibk9E0M8Za9C1affES0rz/RfgEQHc1/ew6RoXb1JO8nY/2aPDp+Bu3RYzOkfKagowMhd4II8wnGEGXg/I4TlG5Y0aTG4+Q1oh49AcD7ggfZ8+UEjLc9sUxniYWlBRZWlugt9PwTcs8sOcuVL433bR/u3vElKiqKbX+50KhpHZOahk3q8sd646iz87a91KhVBYBadatx/dpN3K66AxARcY+YmBiUUiilyJgpAwBZsmQiKDAk1eaNr3CRAuTKbc3fJ5Lvw0+58mXwun03LvPWP11o1LSuSU2jpnXZFJt557Y9cZlr162O21X3eJkj4zKfP3uJ4KDkadfXld/RgdA7gYTHHteXdpykVEPTUePH/z6Me2yVMV2KjLpYFPsYQ4AfMUHG94vHRw5gWbmGSU36Rk48ctmC9t+/AGj3Yr9ZiI42dhQBZWlpHBF9B3ljAp/lfXL0AFaVTPOma+jE41SS93llPi3FHW8ffO74ERUVjfPWvdRvUsukxs8nAHc3T2K0mBfsxbyKOjoQ5B1AiE8QhqhoTu04RvkGlUxqrp+8ypPY92TPCzextjG+Jxuiool+YvxQaWllgdKpdxv+BSo4liZb1iwpHcPsYjTN7P9Swgc1cquU6gwMBjTgMmAA7gMVgHzAUE3TNgPTgI+VUheBVZqmzUquDBnz5eCBf3jc8wcB4eT6tKhJTY5PCpLJ1hq/fRcp+V2z5HrpN2aROxfRgcFxzw3BIVh9UiJBXca6n5H+0zJE3fEl4peFGJ47+WdqVJv7azebPW/2vNZE+j8bOYkMCKOgo8ML66u0q4PboYsAeJ/34ObJa0w8sxiF4sjq3QTdMs/Xuvn+3959x1lRnX8c/3z37ipFihQpAoLYQVTEgIK9x4IRjV0jUX/RKMZGRBONplhiNNHYMBbEEmvQGEQRBARFlCJgRbHQlY6CArvP74+ZC3eXuw1378zcfd6v177WmbkLX66zc8+cOec5bVoxf96CDdsL5i9ir727lX5N222YP28hAMXFxaxcuYqtmzVl+84dwYzHnx1M8xZb88LzL3PvnQ+xfv16Bl3xR0aNH8bq1Wv4fPaXXHPln2KbN1Pffsfw4vMjaiRrWps2G/OkM3cvk7lNxr+ruLiYVStX0axZU7bfoSMGPPncAzRv0YwXnhvO3Xc+WKP5qqNJq61ZkXFer1iwhPZZzut9zzqcA847hlRRIYNPr5n/99VR0LwFJYs3Xi9KlnxD0U67lnpNatvgyVPjW/4JBQWsefIR1k2ZFPx8i5Y0uu4WUm235buH7q31XlA1b0FxmbyFZfO2DfI2uvmfKJ136sa8W/3+FlJttmX1I7Wft6xWbbZh4bxFG7YXzv+aPfbumtMMldm6dXOWLtj4vixdsITOe+1Y7usPPOVQ3hszZcN2szbNufLha2nVsQ1P/mVI5L22LvnypudWUhfgWuAQM9sDuDQ81AboAxxL0KgFuBp4w8z2zNawlXSBpHclvTt69azqBtlkV6kbF4kefziTyTfU+IiIzZclc9kx5mvGTWTesWey4JQL+P7tKbS4cWCp46kWzSjaoRNr3nq3FoOGKnuPM/Q4oQ8dunVm9OBgnFyL7VrReodtua7Xhfy+16/Yab+udP7Jrtl/uOZjbtLTJrK+iFRhin16defiCwZywtFncfQxh9LngJ4UFhZydv9TOPLAk+i+60F8+P4nXHLZ+bHNm6nviUcz7LnhNZJ1Q55s50KZkzfrawwKUyl69urOr8+/ir5HncHRxx5GnwN61Wi+aqnC7yHAW0NHcsuBv2H4zU9wyCU/q/1cZVXl9y+VItW2HSuvuZRvb7uRhpdchRpuBUDJ4m9YMaA/yy44nXqHHoWabl3bgTfdVU7eVdeGeS8unXflpf1Z/qvT2fLgo1CT2s5bWlV+L6OWta+1nIi9f3YA2+++A/+7f9iGfUsXLOGaoy7nigMuYv9+B9O4RZNayek2ZWa1/hWFvGncAocAz5rZYgAzS3efDjOzEjP7AGhVlT/IzAabWQ8z63FIg/LvPrNZvWApDdo227DdoE0z1izceBdatFU9muzSjsOfu5YT3r6DFt07c9Ajl9OsW6dq/T01af3X31DYepsN26ltWlL8TeneiZIVK2Fd8Hju2/8MZ4tddip1vMHhB7L69QmwvrjW8y5fuISmbZtv2G7apjkrs9zp79R7d464+EQGn3frhsde3Y78CV9MncXa1T+wdvUPfDhmGh0r6GH4MRbMX0Tbbdts2G7TthWLMnrIN76mNQCpVIrGjRuxbNkKFsxfxMQJ77Js6XK+X/M9o0e+Qdc9dqPL7kGP+pdfzAHgv8NGsHfPPWObN223rjtTWJhixns1O7Z5fkaedOaFC74u85qFG/5dqVSKRo0bsWzZcubPX8RbE95h6dLlrFnzPaNGjqNbRuZcW7FwKU0yzusm5ZzXae/99y26HJ67yW5pJYu/oaDFxutFQfOWlCxdvMlr1r49HoqLKVm0kJJ5cyhoW3oegS1dwvqvvqBot9I97TXNlnxDqrK8SzLyfr2Q4nlzKGizad7iOV9Q2KV285a1cP7XtN5240dX67bb8HUNDUWqKUsXLtkwzACCnthli5Zu8rouvbtx/MUncft5N224Jmda/vUy5n0yh51/Et3vocsP+dS4FdnvFX8o85patWTabBp1ak3D9i0pKErRsW8v5r668fHLulVreLbrhQzreRnDel7G4imfMeYXt7N0+ucV/Km1a+37H1PYflsK27aGwkIaHnkQa8aWrnaQarGxwV7/wH1Z90XpyWYNjzqE70aMzkner977jJYdW9OsXUtSRSm6H7cfM0aW7jFu16Ujp/7lPB4471a+XbJyw/5l8xezQ8/dKEgVUFCYonPPXVn06dyyf0WNmDZlJp06d6B9h20pKiqi74k/5dWXXy/1mldHvM7Jp/UF4Ji+RzBh3NsAjB01gV277ES9+vVIpVL06t2DWR9/xsIFi9hx5840ax70Hh1w0H58+vHs2OZN69vvpzXeaxtknsH2nbejw3ZB5hP6Zcn88uv8PMx8bN8jmTBuIgBjRo1n1y47Uz/MvG/vfUpNRMu1ue99RouOrdk6PK/3OG5fPhhZenxyi44bG/K7HLIXS75YWPaPqXXrZ31Eqm07CloF14stDziEdZMmlHrN2onjKdx9LwDUuAkFbdtTsnA+Bc1bwhZbBPsbbkXRrl0pnjen1vMWtGlHwTZB3i323zTvuonjKUrnbdSEgm3bU7JoPiqTt3CXrpTUct6yZkz9gI6d2tOuQ1uKigo55oQjGDViXE4zVGb2e5/SulMbWrbfhlRRIb2O68OUke+Ues12XTrR/6Zfcfsvb2Llko3zHJq1bk7RlsF73KBxQ3bssQsLammomNtUCVbrX1HIpzG3o4D/SLrDzJZIalbBa1cBtTJS3IpLeOfaIRz6xECUKuCzf49lxSfz6HZVP5a+93mphm42J7x9B0Vb1adgi0LaHdmD0afdvEmlhRpXXMLSW+5im7tvhoICvn1xBOtmf0mTX53D2g8+Yc24t2h06s+of+C+Qc/GilUsvv7WDT+eatOKVKuW/DB5eu3mDJUUl/DsdQ9x0aPXUJAqYOLTY1g4ay4/vexkvpoxm5mvTabvoDPZokE9zr3nMgCWzVvMA+f/lWnDJ7LTfl25+pXbwIwPx05j5qiK/59sruLiYn438M888dxgClIFPPX4f/jko8+4ctDFvDftfUa+/Dr/Hvocd953M+Mnv8zyZSu46JdXArBixUoG3zOE4aOewjBGj3yDUa8GH2h33HoPz/9vCOvWr2fenAVcdtE1sc4LcNwJR3LWzy8s76/+UZmvuepPPPncv0ilCnjysef5+KNPGXjNJUybOpNXX36dJ4Y+yz/vv4W3poxg+bIV/F//KzZkvv/uRxgx+hnMjFEjx/Haq2MB+P0NV/Kzk46hfoP6THk/+DNuu/nuGs+fqaS4hBeue4TzHh1EQaqAd54ew6JZcznispOYO+NzPnhtMvudcwQ79N6dkvXrWbPiO5664t5azVROUL677+80vuG2oLTWa8Mp/uoL6p/Rn/WzPmLdpDdZN2USRXvtQ5O7h0BJCasfvhdbtZLCPXvQqP9FBP0QYs1/nqL4y5q5Oaso7+rBf6fRH8K8o4ZTPOcL6p/en/Wfhnmnhnn/OQQrLmHNI2HePXrQoP9FwbgLie+H5SBvGcXFxdw46K88+PRdpApSPPvki3z68WwG/Pb/mDntQ0a/Mo7d99yNu4f8lcZNGnPwEfszYOAFHLP/KTnLWFJcwpDr/sXAR6+jIFXA2KdHMW/WHPpdfiqfT/+MKa+9w2nXnE29BvUYcE9wzVgyfzG3n3cTbXdox+m/Oyf9FjN88AvM/firSv7G2nfV9TfzztTpLF++kkNPOJOLfnkW/Y47MupYrooUt7E7P4akc4CrCCaSTQ13vxROIkPSt2a2laQiYATQAnikogllj7U9M1Fv0P6tc9+T82P9bWnzyl8UM8+tmBl1hLxXHNHM7x/j7KY1M0QkV367ey3fONcCJex5Y683v4s6QrX9pEGHqCNU28OTk1eyq6jF9pGXhmjccPtab+Os/G52zv+d+dRzi5kNAYZUcHyr8Ps64NBc5XLOOeecc7mRV41b55xzzjlXNVHVoa1tCXvA45xzzjnnXPm859Y555xzrg4qWxc8X3jPrXPOOeecyxvec+ucc845Vwf5mFvnnHPOOedizntunXPOOefqoHxa6yCT99w655xzzrm84T23zjnnnHN1kFdLcM4555xzLua859Y555xzrg7K1zG33rh1zjnnnKuD8rVx68MSnHPOOedc3vCeW+ecc865Oig/+22959Y555xzzuUR5et4iySQdIGZDY46R1UlLS8kL3PS8oJnzoWk5QXPnAtJywue2eWG99xG64KoA1RT0vJC8jInLS945lxIWl7wzLmQtLzgmV0OeOPWOeecc87lDW/cOuecc865vOGN22glbQxP0vJC8jInLS945lxIWl7wzLmQtLzgmV0O+IQy55xzzjmXN7zn1jnnnHPO5Q1v3DrnnHPOubzhjVvnnHPOOZc3vHHrspJUIGlm1DnqAknNos5QXUnLLOk2SV2izlEdkm6pyr44kdRa0vGSjpPUOuo8VSGpu6QBki6R1D3qPC56ki6tyj4XXz6hLMckbQtsBxSm95nZuOgSlU/S48AgM/sq6izVJWk/oCOl3+dHIwtUAUmzgGnAw8DLloBfyqRllnQecC7B+fAw8KSZrYg2VcUkTTGz7mX2TTezblFlqkj4Hl8HjAYEHAjcaGYPRRqsApKuA04Gng93nQA8Y2Z/ii5VxSQ1Bc5m0+vbgKgyZSNpFVDudcHMGucwTrWU87s31cz2iiqTqx5v3OZQ2OtyCvABUBzuNjM7PrpU5ZM0GtgHmAR8l94f17xpkoYCnQkaX5nvc6wu/mmSBBwG9Ad+AjwFPGJmn0QarAJJzAwgaWeCRu5pwATgATN7PdpUpUm6ELgI2B74LONQI2CCmZ0ZSbBKSPoY2M/MloTbzYE3zWznaJOVT9KHwF5m9n24XR+YYma7RpusfJLeBCYCM4CS9H4zGxJZqApIuhFYCAwluOk5A2hkZrdGGiwLSacBpwN9gDcyDjUCis3ssEiCuWrzxm0OhRf/bmb2Q9RZqkLSJcBcYGnmfjMbG02iqgk/sHaLe29iNpIOBh4DGgLvAVeb2VvRpqpYUjJLSgHHEjRu2wNPE3yIfWdmp0aZLZOkJsDWwE3A1RmHVpnZ0uw/FT1Jo4CjzWxtuL0FMDzODQJJLwOnmdnycLsp8JiZHRttsvJl61WMM0lvm1nPyvbFgaTtgE5k+d0DppvZ+kiCuWorrPwlrgbNBoqARDRugVbApcAU4CHglYQ0GGcCrYEFUQepirCH60zgLGARcAnwIrAn8AzBxTZWkpZZ0u3A8cAo4C9mNik8dEt40xknZmZfSPp12QOSmsW4gTsPeFvSCwSPo/sCkyRdDmBmt0cZrhw/AO9LGkmQ+XBgvKQ7IX6P+kNDJZ0PvETGZ0mMz4tiSWcA/yZ4j09j4xO1WDGzL4EvgX2jzuJ+HO+5zSFJzwF7EHzAZl6U4ngBBTY8fj6CoLerB0Fv14Nm9lmFPxgBSf8luHg2ImhkTaL0+xzL4RSSPiF4ZPewmc0tc+y3Zha7SURJyyypP/BvM1ud5ViTOI2/lfSSmR0r6XOC81kZh83Mto8oWoUkXV/RcTO7IVdZqkrSORUdj+Oj/vCm58/AcjaOaY3zedER+AfQmyDvBOA3ZvZFdKkqJulE4BZgG4LfPxG8x7EdJ+xK88ZtDpV3IY3jBTSTpD0IGrdHAa8DvYCRZjYw0mBlSDqwouNxHU4hSQnpEd8goZm3BnYE6qX3xXUyp3PlkfQZ0NPMFkedJV9J+hQ4zsw+jDqL2zzeuM2xcBzaTuHmx2a2Lso8FZE0ADgHWAz8CxhmZuskFQCzzKxzpAHLIekWM/ttZfviQlJLYCDQhdINr0MiC1WJpGUOZ/JfCrQjmGjYC3grrnkBJPUGppnZd5LOBLoDf49r9RJJPYBr2bQaTCyrOwBIOhb4Ixszx76HTtKLwKnZnkLEkaSdgHuBVmbWVVI34PiYV6SYYGa9o87hNp/Xuc0hSQcBs4C7gXuATyQdEGmoirUATjSzI83smXRD3MxKCCbmxNXhWfYdnfMUVfc48BHBONUbgC+Ad6IMVAVJy3wpQeWPL83sYGAv4JtoI1XqXmB1+ORkIMFYwKHRRqrQ4wRl1voBx2V8xdnfCW7gm5tZYzNrFOeGbagYmCbpfkl3pr+iDlWBB4BBQPrzYzoQmwmc5XhX0lOSTpN0Yvor6lCu6nxCWW79DTjCzD6GDXe0TwJ7R5qqHGZ2XQXHYve4JrOEkqTpGYcaAW9Gk6pKmpvZg5IuDYdOjJUUyyEUGZKW+Xsz+14SkrY0s4/CsmBxtt7MTFJf4B/h+13hGNGIfWNmL0YdoprmADMTNsRmWPiVFA3MbFIwfWODuFcdaAysJphvkmZsrIfsYs4bt7lVlG7YApjZJ5KKogyUZ54AXiZhJZQIezSABZKOAeYTPD6Ps6RlnhuWeRoGjJS0jCBznK2SNIigKsUBYSmzOF8vrpf0LzadMBvnBsFAYHh4Y5aZOY6VHYD4z9HIYrGkzoST3ySdRPwr2RQAl2aUiNuaoHPKJYSPuc0hSQ8R/IKnHy2eCaTM7NzoUuWnsCHQitJj/+I6VvFYgoLh7YG7CHoNbohzL1gSM6eFEw+bACPSNVnjSMHytacD75jZG5I6AAdZfFfaewzYBXifjYsLmJn1jy5VxSS9CnzLpgsixK6yQ1pGFY1SYlwtYXtgMLAfsAz4HDgz5tUSNlmNLNs+F1/euM0hSVsCvyYoHC9gHHBPUhZ1SApJFwN/IKi/mvkhG9uJLa52SGpW0fG49uiHN2evxHkBhLIkzTCz3aPOUR2S3jWzHlHnqI6wxnRaPYLlg5tVNIwsDiQ1BArMbFXUWSoj6T2CG8ll4XYzYGzSzu+6zBu3EQl/WdqFg+tdDQrLuPS0cBnQuJJ0FxWvvR67+sdJy1ymVmwHgp4jAU2Br8wsVotNZApnxZ8Vpxq8FZH0AHCHmX0QdZaqknQzMNrMXo06y48habyZ9Yk6RzaSWgF/Adqa2dGSdgP2NbMHI45WLklnE0yCe5bg+vFz4M9mFucJnS6Dj7nNIUljCFZJKiQoR/SNpLFmdnmkwfLPHCAJDYJ3w++9gd2Ap8Ltk4HJkSSqXKIypxuvku4DXjSz4eH20UDce0W/B2aEq2d9l94ZtxuIDH2Ac8Ibih/YWFYrzk9Mfg0MlLQWWEsySoFlLr1bQLC4TqOI4lTFIwRVNK4Ntz8huG7EtnFrZo9Kehc4hOCcODFJN23Oe25zKj1mJ6y52d7Mrpc0PeYX/8SR9CCwM/A/EjBJRNLrBFU01oXbRcCrYcmqWEpaZkmTzWzvMvti/Ug6aYu+SNou234LljR1NST83Ut/cK8nKMN3m5l9ElmoCkh6x8z2yRyzKmmame0ZdTaXv7znNrcKJbUheMRxbWUvdpvtq/CriHjPLk9rS9Dzkh7/uVW4L86SlnmxpN8BjxE0DM4EYj1sxcyGSKoPdMisshJXZvalpD7Ajmb2cLjQx1ZR56qIgvpUZwCdzOyPktoDbcxsUsTRKnI0QS3hjmz8DD8VuDGqQJX4LhwnnK6W0ItkPFlzCeaN29y6EXgFGG9m74SzSGdFnCkfDQeuofTF34jvxf9mYGrYIwNwIMGEuDjLljm2M8yB04Drgf+E2+PCfbEl6TjgNmALoJOkPYEbzez4aJNlJ+l6gkfkOxM8hi4iuJmI80pP9xBMOj2EYKWybwkW2dknylCVGAYsB6YQDF2Ju8uBF4HOkiYALYGToo3k8p0PS3B5R9LHwJXATEqX94nt49Gw7FPPcPNtM1sYZZ6qSGLmJJE0maDRNSbjcW5sKxJImkaw8tuUjLyxHnYlaYqZdS/zyPw9M9sj6mzlkTTTzLpGnaM6JBUS3PSImC877/KD99zmkKR6wC+BLgQlXACIcx3IhPrGzP4bdYjKSNolXCkrPUFkTvi9raS2ZjYlqmyVkXRjWHrohXC7QNLjZnZGxNGykvRfNq3ysIJggtz9ZhbHHrD1ZraizMpOce6NWBuuqJZ+/Nww6kBVsC4su5bO3JKMG+KYelPS7mY2I+ogVRF+7l1EMOHQgDck3RfT3zmXJ7xxm1tDgY+AIwkekZ8BxG4Z2zyQlJWSrgDOJ/vKN0bQaxdXHSQNMrObwvrNzxA8Jo2r2QSPQ58Mt08hqIO8E/AAcFZEuSoyU9LpQErSjsAA4r2M9NOS7geaSjof6E/w3sbZnQRDVbaR9GeCx+W/jzZSdpJmEFwXCoFzJc0mGVUpHgVWESz2AsFwoKEEFVacqxU+LCGHMqolTDezbuEM81fMLM6NmMRJ4kpJSRNOxHmcYGWng4GXzeyOaFOVT9I4Mzsg2z5J75tZl6iylUdSA4KJp0cQNGBeAf4Y1x4vSbcAr1E672Fm9ttIg1VC0i7AoQSZR5lZLDscyqtGkRbXYVfZhnnEfeiHSz7vuc2t9Dij5ZK6AgsJJj25mrVHXMclZpJ0YkXHY9jTXLbG5j+A+4EJwFhJ3WM8lKKlpA7pJZgVLGXbIjwWyyV4zWw1QeM2KZVVDg8bsiPTOyT9DYht41bSUDM7i+CJWtl9sRLXxmsVTJXUy8wmAkjqSXDNcK7WeOM2twZL2prgsdeLBGVyYr1kYkJNlLRbAopuH1fBMQNi17hl0yEUywgWc/gb8R5KcQUwXtJnBD10nYCLwnGhca0buxPBxMiOZFyr4/akR9KFBGMqt5eUueJiI+LfiCnVYx9OfNq7nNe6zdMTOFvSV+F2B+DD9DCLGA+ncAnmwxJc3pH0IdAZSNJKSa6WhWODdyE4Hz6K6+P9NAXr299HsPJbcXq/mcVqJThJTYCtgZuAqzMOrTKzpdl/KlqSBhGUC6wPrM44tA4YbGaDIgmWh5I6nMIlmzduc0gJXGM7iZK2UlLYOLgeSI8JHUtQzzS2hc6TeC5L2o9Ne0EfjSxQJbKtquZqlqSbgFsJJhamK9iYmY2LLlV+kdQZmGtmP0g6COgGPGpmy6NN5vKZN25zSNLLhGtsm9ke4SOwqUkYH+pqj6TnCGryph+Pn0UwbrjCMblRStq5LGkoQW/+NDb2gpqZDYguVXaSmoX/OQD4mmA2f2bVj1j2hiZRWNVhANCO4NzoBbwVt6EfSRbWP+5BcGP5CsGQvJ3N7KdR5nL5zcfc5lYLM3s6fCSGma2XVFzZD7m819nM+mVs3xB+IMRZ0s7lHsBuloy7+ckE45fTBW6vyjhmwPY5T5S/BhCsRjbRzA4OKyfEeaW9JCoJrw8nAn83s7skTY06lMtv3rjNLV9j22WzRlIfMxsPIKk3sCbiTJVJ2rk8E2gNLIg6SGXMrBMExe/LjgsOC+K7mvO9mX0vCUlbhouq7Bx1qDyzTtJpwNlsnERbFGEeVwd44za3fI1tl82FwJBw7C0EFQjOiTBPVSTtXG4BfCBpEqUf8R8fXaRKvQl0r8I+t/nmSmoKDANGSloGzI84U745F/gV8Gcz+1xSJ+CxiDO5POdjbnNI0skEY47aA/0ISqT8Psa1QV0OhLP4TyIYE9qUoAfUzOzGSINVIknrxUs6MNt+Mxub6yyVkdQa2JagAXA6G4cnNAbuM7NdosqWz8JzpAkwwsxiWfvYOVc13nObW783s2fCWreHEdQGvZegkevqrheA5QTL186LOEuVhKtnV8XgigAABRhJREFUXQ5sZ2bnS9pR0s5m9lLU2bKJYyO2AkcCvyCY5HR7xv5VBOWrXC1I2DkSexnLBWflpRldbfKe2xzKWH73JmCGmT2R3hd1NhcdSTPNrGvUOapD0lMEE5/ONrOukuoTzDLfM+JopUgab2Z9JK2i9AdtuvZx44iiVUpSPzN7Luoczm2OjJKMvw6/Dw2/nwGsjvuTKZds3rjNIUkvEfTMHUawCs4aYJKvsV23SRoM3GVmM6LOUlWS3jWzHpk3Z75efM2TdAzBKlobJpJ5o8AliaQJZta7sn3O1aSCqAPUMT8nGHN7VFjAuhmly/y4OkTSjHC50j7AFEkfS5qesT/O1oa9telqCZ3JmKgVN5J+mWXfzVFkqSpJ9wGnAJcQ9DSfDFS42pNzMdRQUp/0RriYSsMI87g6wHtunYtIkpellHQ48DtgN+BVoDfwCzMbE2Wu8oSLTjxmZo+H2/cA9cysf7TJyidpupl1y/i+FfC8mR0RdTbnqkrS3sBDBJP1IJhf0N8nUrva5I1b51y1hSt+zSAYWjMbeNvMFkebqnxhL/OLBB+yRwNLzew30aaqmKS3zaynpInAicASYKaZ7RhxNOeqTVJjgjZHnOthuzzh1RKcc5vjYYLhFIcTrJg1TdI4M/tHtLFKy1jKFuA8gnqmE4AbJTWL+VK2L4U1WP9KUEnDgH9FG8m56glLHfYjWH63UAoq2/nYcVebvOfWObdZJKUIli49mKBI+5q41WCV9DmbVklIMzNLxFK2YQOhnvd6uaSRNIKgdvdkYMMS3Wb2t8hCubznjVvnXLVJGkUwKeQt4A1gvJl9HW2q7CQVAPua2YSos1RHWEv4CqBDupYwENtaws5lk8RShy75vFqCc25zTAfWAl2BbkC61m3smFkJcFvUOTbDwwQVKPYNt+cCf4oujnOb5U1Ju0cdwtUt3nPrnNts4Qz+c4ErgdZmtmXEkbKSdANBg/x5S8hFz2sJu3wg6QNgB+Bzgpu19AIqvkKZqzU+ocw5V22SLgb2J1iM5EuCKgRvRBqqYpcTDKMolrSGBKxQRsJqCTtXjqOjDuDqHm/cOuc2R33gdmCyma2POkxlzKxR1Bk2w/XACKC9pMcJawlHmsi5KpLU2MxWAquizuLqHh+W4JyrEyQdDxwQbo6J+8SspNUSdi6TpJfM7NiMiiWJrFTikskbt865vBcutbsP8Hi46zSCXuero0tVMUmHENQS3p+wljAQu1rCzlUkvEkbB7xhZh9FncfVDd64dc7lPUnTgT3DygnpGr1T4z6pJQm1hJ2rSJabtKkEDV2/SXO1xhu3zrm8FzZuD0qvSBauXDYmzo3bJNUSdq4ifpPmcs0nlDnn6oK/AFMkjSEY+3cAMCjSRJWbTlCNoivBCk/LJb1lZmuijeVc1WW5SdvHb9JcbfOeW+dc3gvH/c0ClgFfEUzOWhhtqqpJSi1h57KRdAfBTdoPwASC8bd+k+ZqlTdunXN5L4mTs7LUEk5PyhkdaTDnNoPfpLlc8satc65OSNq4P0lXETRoE1FL2Lls/CbNRcEbt865vOeTs5yLht+kuSj4hDLnXF3gk7Oci4CZ/TXqDK7u8Z5b51yd4eP+nHMu/3nPrXMu72UZ9/cQwfAE55xzecYbt865uqA+cDs+7s855/KeD0twzjnnnHN5oyDqAM4555xzztUUb9w655xzzrm84Y1b55xzzjmXN7xx65xzzjnn8oY3bp1zzjnnXN74f+ao9wwYkAJsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb910080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = data.corr().abs()\n",
    "plt.subplots(figsize=(12,9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "sns.heatmap(data_corr,mask=data_corr<1,cbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "单从cnt的角度看，yr,temp,atemp与cnt的相关性是比较高的；从特征的角度看，temp和atemp的相关性已经达到了0.99，和之前的估计一样，是可以删去一个特征的；season和mnth的相关性也比较强，但只有0.83，还没有到直接可以删的地步，等到以后学到pca，或许可以对它进行处理。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来看一看几个与cnt相关性强的相关性视图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xb951f98>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb846390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data,x_vars='atemp',y_vars='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看起来还可以，但毕竟相关性也只有0.63，没法完全贴合线性关系。由于temp和atemp相关性太强，所以只展示一个。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0xbd9ad30>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbd9ad68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data,x_vars='yr',y_vars='cnt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这时候惊奇地发现，2012年地数据不管从分布广度还是数量来说都比2011要大的多，这也解释了为什么cnt的分布图中会有两个小波峰。即便如此，我还是决定先这样做下去，待会儿再考虑是否将数据分开做数据探索。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据探索部分基本上完成了，接下来整理一下，实现数据清理代码就可以了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#直观上感受，体感温度比室外温度更能够影响用户的骑车意愿，所以去掉temp特征，保留atemp特征\n",
    "data = data.drop('temp',axis=1)\n",
    "\n",
    "#去除cnt离群点\n",
    "data = data[data.cnt<8000]\n",
    "data = data[data.cnt>50]\n",
    "\n",
    "#去除hum离群点\n",
    "data = data[data.hum>0.3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据处理\n",
    "按照上面的分析，要对特征做归一化，对cnt做标准化。这里对分类特征暂时不做特别处理，先把它们和数值型特征一起归一化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n",
      "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "#对特征做归一化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mms = MinMaxScaler()\n",
    "x_train = mms.fit_transform(x_train)\n",
    "x_test = mms.transform(x_test)\n",
    "\n",
    "#对cnt做标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler()\n",
    "y_train = ss.fit_transform(y_train.reshape(-1,1))\n",
    "y_test = ss.transform(y_test.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 最小二乘线性回归\n",
    "下面用最小二乘线性回归进行拟合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train,y_train) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用模型预测训练集和测试集的y，并用r2_score评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集r2_score评分： 0.758519666910396\n",
      "测试集r2_score评分： -0.6965661915185128\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = lr.predict(x_train)\n",
    "y_test_pred = lr.predict(x_test)\n",
    "\n",
    "print(\"训练集r2_score评分：\",r2_score(y_train,y_train_pred))\n",
    "print(\"测试集r2_score评分：\",r2_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个结果是令人惊讶的，经过分析，发现主要原因是2012年的cnt比2011年的cnt要大的多，显然的，有一些这份数据以外的因素导致cnt的增长，因此，无论如何努力，都无法使得测试集上的r2_score追上训练集上的r2_score。但是，r2_score为负的情况实在让人没有办法接受。有什么方法能够改进吗？我找到了一种不算很正规但我觉得应该没错的方法。\n",
    "\n",
    "　　通过观察，发现在做数据标准化的时候，训练数据(尤其是y_test)使用训练数据的StandardScaler来处理的，换句话说，实际上y_test标准化处理时，用的均值和方差都是y_train的均值和方差。这显然和事实是不相符的。即便两者方差的误差是可以接受的，但均值的差距可不是一星半点，毕竟y_test有很大一部分聚集在8000附近。这样就明白了，在做标准化的时候，不能用训练集的标准化模型来处理测试集(仅限于测试集和训练集的误差太大的情况)。那不对y做标准化可以吗？我觉得也不太好。因为即使不做标准化，最后线性拟合的结果也是不好的，因为未知因素的影响实在太大了。经过斟酌，我想如果将测试数据与训练数据分开做标准化是不是会好一些呢？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据处理与线性回归改进版"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集r2_score评分： 0.758519666910396\n",
      "测试集r2_score评分： 0.6671355476503027\n"
     ]
    }
   ],
   "source": [
    "#对特征做归一化\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "mms = MinMaxScaler()\n",
    "x_train = mms.fit_transform(x_train)\n",
    "x_test = mms.transform(x_test)\n",
    "\n",
    "#对y_test和y_train分开做标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_train = StandardScaler()\n",
    "ss_test = StandardScaler()\n",
    "y_train = ss_train.fit_transform(y_train.reshape(-1,1))\n",
    "y_test = ss_train.fit_transform(y_test.reshape(-1,1))\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train,y_train)\n",
    "\n",
    "y_train_pred = lr.predict(x_train)\n",
    "y_test_pred = lr.predict(x_test)\n",
    "\n",
    "\n",
    "print(\"训练集r2_score评分：\",r2_score(y_train,y_train_pred))\n",
    "print(\"测试集r2_score评分：\",r2_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据处理改进版——独热编码\n",
    "其实训练集的结果也不算太好，于是我决定着手处理分类型特征。查来查去，发现好像独热编码用的比较多。大概的作用就是将分类型数值进行扩维，使得分类特征的数据都只用0和1来表示，这样就避免了分类数值较大的样本对结果的影响。比如season特征，原本有1,2,3,4，四个取值，经过独热编码，变成了[1,0,0,0]，[0,1,0,0]，[0,0,1,0]，[0,0,0,1]四种取值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'numpy.ndarray' object has no attribute 'drop'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-40062f610f46>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#分离出数值型数据，并对数值型数据做归一化\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mx_train_type\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx_train\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'hum'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'atemp'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'windspeed'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mx_train_num\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx_train\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'hum'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'atemp'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'windspeed'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mx_test_type\u001b[0m \u001b[1;33m=\u001b[0m  \u001b[0mx_test\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'hum'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'atemp'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'windspeed'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mx_test_num\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'hum'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'atemp'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'windspeed'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'drop'"
     ]
    }
   ],
   "source": [
    "#分离出数值型数据，并对数值型数据做归一化\n",
    "x_train_type = x_train.drop(['hum','atemp','windspeed'],axis=1)\n",
    "x_train_num = x_train[['hum','atemp','windspeed']]\n",
    "x_test_type =  x_test.drop(['hum','atemp','windspeed'],axis=1)\n",
    "x_test_num = x_test[['hum','atemp','windspeed']]\n",
    "\n",
    "#对数值型数据做标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_x_train = StandardScaler()\n",
    "ss_x_test = StandardScaler()\n",
    "x_train_num = ss_x_train.fit_transform(x_train_num)\n",
    "x_test_num = ss_x_test.fit_transform(x_test_num)\n",
    "\n",
    "#对类别型特征做独热编码\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "enc_train = OneHotEncoder()\n",
    "enc_test = OneHotEncoder()\n",
    "x_train_type = enc_train.fit_transform(x_train_type).toarray()\n",
    "x_test_type = enc_test.fit_transform(x_test_type).toarray()\n",
    "\n",
    "#还原特征矩阵\n",
    "x_train = np.concatenate((x_train_type,x_train_num),axis=1)\n",
    "x_test = np.concatenate((x_test_type,x_test_num),axis=1)\n",
    "\n",
    "#对cnt做标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_y_train = StandardScaler()\n",
    "ss_y_test = StandardScaler()\n",
    "y_train = ss_y_train.fit_transform(y_train.reshape(-1,1))\n",
    "y_test = ss_y_test.fit_transform(y_test.reshape(-1,1))\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr = LinearRegression()\n",
    "lr.fit(x_train,y_train)\n",
    "\n",
    "y_train_pred = lr.predict(x_train)\n",
    "y_test_pred = lr.predict(x_test)\n",
    "\n",
    "print(\"训练集r2_score评分：\",r2_score(y_train,y_train_pred))\n",
    "print(\"测试集r2_score评分：\",r2_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集r2_score评分： 0.7574336108170601\n",
      "测试集r2_score评分： 0.6699533520409138\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.metrics import r2_score\n",
    "alphas = [0.01,0.1,1,10]\n",
    "lr = RidgeCV(alphas=alphas,store_cv_values=True)\n",
    "lr.fit(x_train,y_train)\n",
    "y_train_pred = lr.predict(x_train)\n",
    "y_test_pred = lr.predict(x_test)\n",
    "print(\"训练集r2_score评分：\",r2_score(y_train,y_train_pred))\n",
    "print(\"测试集r2_score评分：\",r2_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "性能好了不少，测试集的评分也略有升高，说明正则项还是起到了一定的作用的。接下来看一看它的正则项是否能调优。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最佳正则参数为： 1.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc670a90>]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6381d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"最佳正则参数为：\",lr.alpha_)\n",
    "mse_mean = np.mean(lr.cv_values_,axis=0)\n",
    "plt.plot(np.log10(alphas),mse_mean.reshape(len(alphas),1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.748113832681577\n",
      "0.6459959146846996\n"
     ]
    }
   ],
   "source": [
    "#Lasso回归\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "alphas = [0.01,0.1,1,10,100]\n",
    "ls =LassoCV(alphas=alphas)\n",
    "ls.fit(x_train,y_train)\n",
    "\n",
    "y_train_pred = ls.predict(x_train)\n",
    "y_test_pred = ls.predict(x_test)\n",
    "\n",
    "print(r2_score(y_train,y_train_pred))\n",
    "print(r2_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
